# Import packages
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Set style & figures inline
sns.set()
%matplotlib inline
!pip install pandas
!pip install numpy
!pip install plotly
!pip install requests
!pip install beautifulsoup4
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import requests
from bs4 import BeautifulSoup
import datetime
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confirmed_cases_data = 'time_series_covid19_confirmed_global.csv'
death_cases_data = 'time_series_covid19_deaths_global.csv'
recovery_cases_data = 'time_series_covid19_recovered_global.csv'
# Import datasets as pandas dataframes
raw_data_confirmed = pd.read_csv(confirmed_cases_data)
raw_data_deaths = pd.read_csv(death_cases_data)
raw_data_recovered = pd.read_csv(recovery_cases_data)
Langkah selanjutnya dalam proses explorasi dan visualisai data covid-19 adalah menampilkan beberapa baris teratas, informasi, dan deskriptif statistik dataframe raw_data_confirmed
raw_data_confirmed.head(10)
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | ... | 3/29/20 | 3/30/20 | 3/31/20 | 4/1/20 | 4/2/20 | 4/3/20 | 4/4/20 | 4/5/20 | 4/6/20 | 4/7/20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | Afghanistan | 33.0000 | 65.0000 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 120 | 170 | 174 | 237 | 273 | 281 | 299 | 349 | 367 | 423 |
| 1 | NaN | Albania | 41.1533 | 20.1683 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 212 | 223 | 243 | 259 | 277 | 304 | 333 | 361 | 377 | 383 |
| 2 | NaN | Algeria | 28.0339 | 1.6596 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 511 | 584 | 716 | 847 | 986 | 1171 | 1251 | 1320 | 1423 | 1468 |
| 3 | NaN | Andorra | 42.5063 | 1.5218 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 334 | 370 | 376 | 390 | 428 | 439 | 466 | 501 | 525 | 545 |
| 4 | NaN | Angola | -11.2027 | 17.8739 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 7 | 7 | 7 | 8 | 8 | 8 | 10 | 14 | 16 | 17 |
| 5 | NaN | Antigua and Barbuda | 17.0608 | -61.7964 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 7 | 7 | 7 | 7 | 9 | 15 | 15 | 15 | 15 | 19 |
| 6 | NaN | Argentina | -38.4161 | -63.6167 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 745 | 820 | 1054 | 1054 | 1133 | 1265 | 1451 | 1451 | 1554 | 1628 |
| 7 | NaN | Armenia | 40.0691 | 45.0382 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 424 | 482 | 532 | 571 | 663 | 736 | 770 | 822 | 833 | 853 |
| 8 | Australian Capital Territory | Australia | -35.4735 | 149.0124 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 77 | 78 | 80 | 84 | 87 | 91 | 93 | 96 | 96 | 96 |
| 9 | New South Wales | Australia | -33.8688 | 151.2093 | 0 | 0 | 0 | 0 | 3 | 4 | ... | 1791 | 2032 | 2032 | 2182 | 2298 | 2389 | 2493 | 2580 | 2637 | 2686 |
10 rows × 81 columns
raw_data_confirmed.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 263 entries, 0 to 262 Data columns (total 81 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Province/State 82 non-null object 1 Country/Region 263 non-null object 2 Lat 263 non-null float64 3 Long 263 non-null float64 4 1/22/20 263 non-null int64 5 1/23/20 263 non-null int64 6 1/24/20 263 non-null int64 7 1/25/20 263 non-null int64 8 1/26/20 263 non-null int64 9 1/27/20 263 non-null int64 10 1/28/20 263 non-null int64 11 1/29/20 263 non-null int64 12 1/30/20 263 non-null int64 13 1/31/20 263 non-null int64 14 2/1/20 263 non-null int64 15 2/2/20 263 non-null int64 16 2/3/20 263 non-null int64 17 2/4/20 263 non-null int64 18 2/5/20 263 non-null int64 19 2/6/20 263 non-null int64 20 2/7/20 263 non-null int64 21 2/8/20 263 non-null int64 22 2/9/20 263 non-null int64 23 2/10/20 263 non-null int64 24 2/11/20 263 non-null int64 25 2/12/20 263 non-null int64 26 2/13/20 263 non-null int64 27 2/14/20 263 non-null int64 28 2/15/20 263 non-null int64 29 2/16/20 263 non-null int64 30 2/17/20 263 non-null int64 31 2/18/20 263 non-null int64 32 2/19/20 263 non-null int64 33 2/20/20 263 non-null int64 34 2/21/20 263 non-null int64 35 2/22/20 263 non-null int64 36 2/23/20 263 non-null int64 37 2/24/20 263 non-null int64 38 2/25/20 263 non-null int64 39 2/26/20 263 non-null int64 40 2/27/20 263 non-null int64 41 2/28/20 263 non-null int64 42 2/29/20 263 non-null int64 43 3/1/20 263 non-null int64 44 3/2/20 263 non-null int64 45 3/3/20 263 non-null int64 46 3/4/20 263 non-null int64 47 3/5/20 263 non-null int64 48 3/6/20 263 non-null int64 49 3/7/20 263 non-null int64 50 3/8/20 263 non-null int64 51 3/9/20 263 non-null int64 52 3/10/20 263 non-null int64 53 3/11/20 263 non-null int64 54 3/12/20 263 non-null int64 55 3/13/20 263 non-null int64 56 3/14/20 263 non-null int64 57 3/15/20 263 non-null int64 58 3/16/20 263 non-null int64 59 3/17/20 263 non-null int64 60 3/18/20 263 non-null int64 61 3/19/20 263 non-null int64 62 3/20/20 263 non-null int64 63 3/21/20 263 non-null int64 64 3/22/20 263 non-null int64 65 3/23/20 263 non-null int64 66 3/24/20 263 non-null int64 67 3/25/20 263 non-null int64 68 3/26/20 263 non-null int64 69 3/27/20 263 non-null int64 70 3/28/20 263 non-null int64 71 3/29/20 263 non-null int64 72 3/30/20 263 non-null int64 73 3/31/20 263 non-null int64 74 4/1/20 263 non-null int64 75 4/2/20 263 non-null int64 76 4/3/20 263 non-null int64 77 4/4/20 263 non-null int64 78 4/5/20 263 non-null int64 79 4/6/20 263 non-null int64 80 4/7/20 263 non-null int64 dtypes: float64(2), int64(77), object(2) memory usage: 166.6+ KB
raw_data_confirmed.describe()
| Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | 1/28/20 | 1/29/20 | ... | 3/29/20 | 3/30/20 | 3/31/20 | 4/1/20 | 4/2/20 | 4/3/20 | 4/4/20 | 4/5/20 | 4/6/20 | 4/7/20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 263.000000 | 263.000000 | 263.000000 | 263.000000 | 263.000000 | 263.000000 | 263.000000 | 263.000000 | 263.000000 | 263.000000 | ... | 263.000000 | 263.000000 | 263.000000 | 263.000000 | 263.000000 | 263.000000 | 263.000000 | 263.000000 | 263.000000 | 263.000000 |
| mean | 21.339244 | 22.068133 | 2.110266 | 2.486692 | 3.577947 | 5.452471 | 8.053232 | 11.129278 | 21.209125 | 23.444867 | ... | 2738.174905 | 2974.885932 | 3260.406844 | 3546.026616 | 3852.927757 | 4166.984791 | 4552.870722 | 4836.939163 | 5114.452471 | 5422.418251 |
| std | 24.779585 | 70.785949 | 27.434015 | 27.532888 | 34.275498 | 47.702207 | 66.662110 | 89.815834 | 220.427512 | 221.769901 | ... | 13348.022358 | 14659.339365 | 16274.718201 | 17892.269613 | 19740.409389 | 21707.026686 | 23983.928488 | 25717.561274 | 27517.452168 | 29418.401918 |
| min | -51.796300 | -135.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 6.938500 | -21.031300 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 12.000000 | 14.000000 | 15.000000 | 17.000000 | 19.500000 | 20.500000 | 21.000000 | 22.000000 | 24.000000 | 27.000000 |
| 50% | 23.634500 | 20.168300 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 126.000000 | 139.000000 | 143.000000 | 168.000000 | 176.000000 | 184.000000 | 195.000000 | 214.000000 | 226.000000 | 237.000000 |
| 75% | 41.178850 | 79.500000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 642.500000 | 685.500000 | 715.000000 | 780.000000 | 881.000000 | 949.000000 | 983.500000 | 1020.000000 | 1068.500000 | 1135.500000 |
| max | 71.706900 | 178.065000 | 444.000000 | 444.000000 | 549.000000 | 761.000000 | 1058.000000 | 1423.000000 | 3554.000000 | 3554.000000 | ... | 140909.000000 | 161837.000000 | 188172.000000 | 213372.000000 | 243616.000000 | 275586.000000 | 308850.000000 | 337072.000000 | 366667.000000 | 396223.000000 |
8 rows × 79 columns
Informasi diatas masih sangat general sehingga anda perlu mendapatkan informasi yang lebih spesifik, salah satunya mendapatkan informasi jumlah kasus terkonfirmasi COVID-19 berdasarkan kriteria tertentu. Anda perlu menampilkan dataframeconfirmed_countryyang berisi jumlah kasus terkonfirmasi COVID-19 di setiap negara berdasarkan deret waktu(time series) yang terindeks berdasarkan waktu(date/time) bukan berdasarkan Country/Region.
confirmed_country = raw_data_confirmed.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
value_vars=raw_data_confirmed.iloc[:,4:],
var_name='Date', value_name='Total Pasien Terkonfirmasi')
confirmed_country.head(10)
| Province/State | Country/Region | Lat | Long | Date | Total Pasien Terkonfirmasi | |
|---|---|---|---|---|---|---|
| 0 | NaN | Afghanistan | 33.0000 | 65.0000 | 1/22/20 | 0 |
| 1 | NaN | Albania | 41.1533 | 20.1683 | 1/22/20 | 0 |
| 2 | NaN | Algeria | 28.0339 | 1.6596 | 1/22/20 | 0 |
| 3 | NaN | Andorra | 42.5063 | 1.5218 | 1/22/20 | 0 |
| 4 | NaN | Angola | -11.2027 | 17.8739 | 1/22/20 | 0 |
| 5 | NaN | Antigua and Barbuda | 17.0608 | -61.7964 | 1/22/20 | 0 |
| 6 | NaN | Argentina | -38.4161 | -63.6167 | 1/22/20 | 0 |
| 7 | NaN | Armenia | 40.0691 | 45.0382 | 1/22/20 | 0 |
| 8 | Australian Capital Territory | Australia | -35.4735 | 149.0124 | 1/22/20 | 0 |
| 9 | New South Wales | Australia | -33.8688 | 151.2093 | 1/22/20 | 0 |
confirmed_country["Date"] = pd.to_datetime(confirmed_country["Date"])
confirmed_country
| Province/State | Country/Region | Lat | Long | Date | Total Pasien Terkonfirmasi | |
|---|---|---|---|---|---|---|
| 0 | NaN | Afghanistan | 33.00000 | 65.000000 | 2020-01-22 | 0 |
| 1 | NaN | Albania | 41.15330 | 20.168300 | 2020-01-22 | 0 |
| 2 | NaN | Algeria | 28.03390 | 1.659600 | 2020-01-22 | 0 |
| 3 | NaN | Andorra | 42.50630 | 1.521800 | 2020-01-22 | 0 |
| 4 | NaN | Angola | -11.20270 | 17.873900 | 2020-01-22 | 0 |
| ... | ... | ... | ... | ... | ... | ... |
| 20246 | Falkland Islands (Malvinas) | United Kingdom | -51.79630 | -59.523600 | 2020-04-07 | 2 |
| 20247 | Saint Pierre and Miquelon | France | 46.88520 | -56.315900 | 2020-04-07 | 1 |
| 20248 | NaN | South Sudan | 6.87700 | 31.307000 | 2020-04-07 | 2 |
| 20249 | NaN | Western Sahara | 24.21550 | -12.885800 | 2020-04-07 | 4 |
| 20250 | NaN | Sao Tome and Principe | 0.18636 | 6.613081 | 2020-04-07 | 4 |
20251 rows × 6 columns
confirmed_country = confirmed_country.drop(["Province/State","Lat","Long"], axis = 1)
confirmed_country.head(10)
| Country/Region | Date | Total Pasien Terkonfirmasi | |
|---|---|---|---|
| 0 | Afghanistan | 2020-01-22 | 0 |
| 1 | Albania | 2020-01-22 | 0 |
| 2 | Algeria | 2020-01-22 | 0 |
| 3 | Andorra | 2020-01-22 | 0 |
| 4 | Angola | 2020-01-22 | 0 |
| 5 | Antigua and Barbuda | 2020-01-22 | 0 |
| 6 | Argentina | 2020-01-22 | 0 |
| 7 | Armenia | 2020-01-22 | 0 |
| 8 | Australia | 2020-01-22 | 0 |
| 9 | Australia | 2020-01-22 | 0 |
confirmed_country = confirmed_country.groupby(['Date','Country/Region'],as_index=False).agg({'Total Pasien Terkonfirmasi': 'sum'})
confirmed_country.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 14168 entries, 0 to 14167 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Date 14168 non-null datetime64[ns] 1 Country/Region 14168 non-null object 2 Total Pasien Terkonfirmasi 14168 non-null int64 dtypes: datetime64[ns](1), int64(1), object(1) memory usage: 332.2+ KB
confirmed_country = confirmed_country.pivot_table(index=['Date'], columns='Country/Region',
values='Total Pasien Terkonfirmasi', aggfunc='first').reset_index()
confirmed_country
| Country/Region | Date | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | ... | United Arab Emirates | United Kingdom | Uruguay | Uzbekistan | Venezuela | Vietnam | West Bank and Gaza | Western Sahara | Zambia | Zimbabwe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2020-01-22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 1 | 2020-01-23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 2 | 2020-01-24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 3 | 2020-01-25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 4 | 2020-01-26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | ... | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 72 | 2020-04-03 | 281 | 304 | 1171 | 439 | 8 | 15 | 1265 | 736 | 5330 | ... | 1264 | 38689 | 369 | 227 | 153 | 237 | 194 | 0 | 39 | 9 |
| 73 | 2020-04-04 | 299 | 333 | 1251 | 466 | 10 | 15 | 1451 | 770 | 5550 | ... | 1505 | 42477 | 400 | 266 | 155 | 240 | 217 | 0 | 39 | 9 |
| 74 | 2020-04-05 | 349 | 361 | 1320 | 501 | 14 | 15 | 1451 | 822 | 5687 | ... | 1799 | 48436 | 400 | 342 | 159 | 241 | 237 | 4 | 39 | 9 |
| 75 | 2020-04-06 | 367 | 377 | 1423 | 525 | 16 | 15 | 1554 | 833 | 5797 | ... | 2076 | 52279 | 406 | 457 | 165 | 245 | 254 | 4 | 39 | 10 |
| 76 | 2020-04-07 | 423 | 383 | 1468 | 545 | 17 | 19 | 1628 | 853 | 5895 | ... | 2359 | 55949 | 424 | 520 | 165 | 249 | 261 | 4 | 39 | 11 |
77 rows × 185 columns
pd.set_option('display.max_columns', None)
confirmed_country = confirmed_country.set_index(['Date'])
confirmed_country
| Country/Region | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | Austria | Azerbaijan | Bahamas | Bahrain | Bangladesh | Barbados | Belarus | Belgium | Belize | Benin | Bhutan | Bolivia | Bosnia and Herzegovina | Botswana | Brazil | Brunei | Bulgaria | Burkina Faso | Burma | Burundi | Cabo Verde | Cambodia | Cameroon | Canada | Central African Republic | Chad | Chile | China | Colombia | Congo (Brazzaville) | Congo (Kinshasa) | Costa Rica | Cote d'Ivoire | Croatia | Cuba | Cyprus | Czechia | Denmark | Diamond Princess | Djibouti | Dominica | Dominican Republic | Ecuador | Egypt | El Salvador | Equatorial Guinea | Eritrea | Estonia | Eswatini | Ethiopia | Fiji | Finland | France | Gabon | Gambia | Georgia | Germany | Ghana | Greece | Grenada | Guatemala | Guinea | Guinea-Bissau | Guyana | Haiti | Holy See | Honduras | Hungary | Iceland | India | Indonesia | Iran | Iraq | Ireland | Israel | Italy | Jamaica | Japan | Jordan | Kazakhstan | Kenya | Korea, South | Kosovo | Kuwait | Kyrgyzstan | Laos | Latvia | Lebanon | Liberia | Libya | Liechtenstein | Lithuania | Luxembourg | MS Zaandam | Madagascar | Malawi | Malaysia | Maldives | Mali | Malta | Mauritania | Mauritius | Mexico | Moldova | Monaco | Mongolia | Montenegro | Morocco | Mozambique | Namibia | Nepal | Netherlands | New Zealand | Nicaragua | Niger | Nigeria | North Macedonia | Norway | Oman | Pakistan | Panama | Papua New Guinea | Paraguay | Peru | Philippines | Poland | Portugal | Qatar | Romania | Russia | Rwanda | Saint Kitts and Nevis | Saint Lucia | Saint Vincent and the Grenadines | San Marino | Sao Tome and Principe | Saudi Arabia | Senegal | Serbia | Seychelles | Sierra Leone | Singapore | Slovakia | Slovenia | Somalia | South Africa | South Sudan | Spain | Sri Lanka | Sudan | Suriname | Sweden | Switzerland | Syria | Taiwan* | Tanzania | Thailand | Timor-Leste | Togo | Trinidad and Tobago | Tunisia | Turkey | US | Uganda | Ukraine | United Arab Emirates | United Kingdom | Uruguay | Uzbekistan | Venezuela | Vietnam | West Bank and Gaza | Western Sahara | Zambia | Zimbabwe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 2020-01-22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 548 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 643 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 2020-01-24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 920 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 2020-01-25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1406 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| 2020-01-26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 2075 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 8 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2020-04-03 | 281 | 304 | 1171 | 439 | 8 | 15 | 1265 | 736 | 5330 | 11524 | 443 | 24 | 672 | 61 | 51 | 351 | 16770 | 4 | 16 | 5 | 132 | 579 | 4 | 9056 | 134 | 485 | 302 | 20 | 3 | 6 | 114 | 509 | 12437 | 8 | 8 | 3737 | 82511 | 1267 | 22 | 134 | 416 | 218 | 1079 | 269 | 396 | 4091 | 3946 | 712 | 49 | 12 | 1488 | 3368 | 985 | 46 | 16 | 22 | 961 | 9 | 35 | 7 | 1615 | 65202 | 21 | 4 | 155 | 91159 | 205 | 1613 | 12 | 50 | 73 | 15 | 23 | 18 | 7 | 222 | 623 | 1364 | 2567 | 1986 | 53183 | 820 | 4273 | 7428 | 119827 | 47 | 2617 | 310 | 464 | 122 | 10062 | 126 | 417 | 130 | 10 | 493 | 508 | 7 | 11 | 75 | 696 | 2612 | 9 | 70 | 3 | 3333 | 19 | 39 | 202 | 6 | 186 | 1510 | 591 | 64 | 14 | 174 | 791 | 10 | 14 | 6 | 15821 | 868 | 5 | 120 | 210 | 430 | 5370 | 252 | 2686 | 1475 | 1 | 92 | 1595 | 3018 | 3383 | 9886 | 1075 | 3183 | 4149 | 89 | 9 | 13 | 3 | 245 | 0 | 2039 | 207 | 1476 | 10 | 2 | 1114 | 450 | 934 | 7 | 1505 | 0 | 119199 | 159 | 10 | 10 | 6131 | 19606 | 16 | 348 | 20 | 1978 | 1 | 40 | 98 | 495 | 20921 | 275586 | 48 | 1072 | 1264 | 38689 | 369 | 227 | 153 | 237 | 194 | 0 | 39 | 9 |
| 2020-04-04 | 299 | 333 | 1251 | 466 | 10 | 15 | 1451 | 770 | 5550 | 11781 | 521 | 28 | 688 | 70 | 52 | 440 | 18431 | 4 | 16 | 5 | 139 | 624 | 4 | 10360 | 135 | 503 | 318 | 21 | 3 | 7 | 114 | 555 | 12978 | 8 | 9 | 4161 | 82543 | 1406 | 22 | 154 | 435 | 245 | 1126 | 288 | 426 | 4472 | 4269 | 712 | 50 | 14 | 1488 | 3465 | 1070 | 56 | 16 | 29 | 1039 | 9 | 38 | 12 | 1882 | 90848 | 21 | 4 | 162 | 96092 | 205 | 1673 | 12 | 61 | 111 | 18 | 23 | 20 | 7 | 264 | 678 | 1417 | 3082 | 2092 | 55743 | 878 | 4604 | 7851 | 124632 | 53 | 3139 | 323 | 531 | 126 | 10156 | 135 | 479 | 144 | 10 | 509 | 520 | 10 | 18 | 77 | 771 | 2729 | 9 | 70 | 4 | 3483 | 19 | 41 | 213 | 6 | 196 | 1688 | 752 | 66 | 14 | 201 | 919 | 10 | 14 | 9 | 16727 | 950 | 5 | 144 | 214 | 483 | 5550 | 277 | 2818 | 1673 | 1 | 96 | 1746 | 3094 | 3627 | 10524 | 1325 | 3613 | 4731 | 102 | 9 | 14 | 7 | 259 | 0 | 2179 | 219 | 1624 | 10 | 4 | 1189 | 471 | 977 | 7 | 1585 | 0 | 126168 | 166 | 10 | 10 | 6443 | 20505 | 16 | 355 | 20 | 2067 | 1 | 41 | 103 | 553 | 23934 | 308850 | 48 | 1225 | 1505 | 42477 | 400 | 266 | 155 | 240 | 217 | 0 | 39 | 9 |
| 2020-04-05 | 349 | 361 | 1320 | 501 | 14 | 15 | 1451 | 822 | 5687 | 12051 | 584 | 28 | 700 | 88 | 56 | 562 | 19691 | 5 | 22 | 5 | 157 | 654 | 6 | 11130 | 135 | 531 | 345 | 21 | 3 | 7 | 114 | 650 | 15756 | 8 | 9 | 4471 | 82602 | 1485 | 45 | 154 | 454 | 261 | 1182 | 320 | 446 | 4587 | 4561 | 712 | 59 | 14 | 1745 | 3646 | 1173 | 62 | 16 | 29 | 1097 | 9 | 43 | 12 | 1927 | 93773 | 21 | 4 | 174 | 100123 | 214 | 1735 | 12 | 61 | 121 | 18 | 24 | 21 | 7 | 268 | 733 | 1486 | 3588 | 2273 | 58226 | 961 | 4994 | 8430 | 128948 | 58 | 3139 | 345 | 584 | 142 | 10237 | 145 | 556 | 147 | 11 | 533 | 527 | 13 | 18 | 77 | 811 | 2804 | 9 | 72 | 4 | 3662 | 19 | 45 | 227 | 6 | 227 | 1890 | 864 | 73 | 14 | 214 | 1021 | 10 | 16 | 9 | 17953 | 1039 | 6 | 184 | 232 | 555 | 5687 | 298 | 3157 | 1801 | 1 | 104 | 2281 | 3246 | 4102 | 11278 | 1604 | 3864 | 5389 | 104 | 10 | 14 | 7 | 266 | 0 | 2402 | 222 | 1908 | 10 | 6 | 1309 | 485 | 997 | 7 | 1655 | 1 | 131646 | 176 | 12 | 10 | 6830 | 21100 | 19 | 363 | 22 | 2169 | 1 | 44 | 104 | 574 | 27069 | 337072 | 52 | 1308 | 1799 | 48436 | 400 | 342 | 159 | 241 | 237 | 4 | 39 | 9 |
| 2020-04-06 | 367 | 377 | 1423 | 525 | 16 | 15 | 1554 | 833 | 5797 | 12297 | 641 | 29 | 756 | 123 | 60 | 700 | 20814 | 7 | 26 | 5 | 183 | 674 | 6 | 12161 | 135 | 549 | 364 | 22 | 3 | 7 | 114 | 658 | 16563 | 8 | 9 | 4815 | 82665 | 1579 | 45 | 161 | 467 | 323 | 1222 | 350 | 465 | 4822 | 4875 | 712 | 90 | 15 | 1828 | 3747 | 1322 | 69 | 16 | 31 | 1108 | 10 | 44 | 14 | 2176 | 98963 | 24 | 4 | 188 | 103374 | 214 | 1755 | 12 | 70 | 128 | 18 | 31 | 24 | 7 | 298 | 744 | 1562 | 4778 | 2491 | 60500 | 1031 | 5364 | 8904 | 132547 | 58 | 3654 | 349 | 662 | 158 | 10284 | 145 | 665 | 216 | 12 | 542 | 541 | 14 | 19 | 77 | 843 | 2843 | 9 | 82 | 5 | 3793 | 19 | 47 | 241 | 6 | 244 | 2143 | 965 | 77 | 15 | 233 | 1120 | 10 | 16 | 9 | 18926 | 1106 | 6 | 253 | 238 | 570 | 5865 | 331 | 3766 | 1988 | 2 | 113 | 2561 | 3660 | 4413 | 11730 | 1832 | 4057 | 6343 | 105 | 10 | 14 | 7 | 266 | 4 | 2605 | 226 | 2200 | 11 | 6 | 1375 | 534 | 1021 | 7 | 1686 | 1 | 136675 | 178 | 12 | 10 | 7206 | 21657 | 19 | 373 | 24 | 2220 | 1 | 58 | 105 | 596 | 30217 | 366667 | 52 | 1319 | 2076 | 52279 | 406 | 457 | 165 | 245 | 254 | 4 | 39 | 10 |
| 2020-04-07 | 423 | 383 | 1468 | 545 | 17 | 19 | 1628 | 853 | 5895 | 12639 | 717 | 33 | 811 | 164 | 63 | 861 | 22194 | 7 | 26 | 5 | 194 | 764 | 6 | 14034 | 135 | 577 | 384 | 22 | 3 | 7 | 115 | 658 | 17872 | 8 | 10 | 5116 | 82718 | 1780 | 45 | 180 | 483 | 349 | 1282 | 396 | 494 | 5017 | 5266 | 712 | 90 | 15 | 1956 | 3747 | 1450 | 78 | 16 | 31 | 1149 | 10 | 52 | 15 | 2308 | 110065 | 30 | 4 | 196 | 107663 | 287 | 1832 | 12 | 77 | 144 | 33 | 33 | 25 | 7 | 305 | 817 | 1586 | 5311 | 2738 | 62589 | 1122 | 5709 | 9248 | 135586 | 63 | 3906 | 353 | 697 | 172 | 10331 | 170 | 743 | 228 | 14 | 548 | 548 | 14 | 20 | 78 | 880 | 2970 | 9 | 88 | 8 | 3963 | 19 | 56 | 293 | 6 | 268 | 2439 | 1056 | 79 | 15 | 241 | 1184 | 10 | 16 | 9 | 19709 | 1160 | 6 | 278 | 254 | 599 | 6086 | 371 | 4035 | 2100 | 2 | 115 | 2954 | 3764 | 4848 | 12442 | 2057 | 4417 | 7497 | 105 | 11 | 14 | 8 | 279 | 4 | 2795 | 237 | 2447 | 11 | 6 | 1481 | 581 | 1059 | 8 | 1749 | 2 | 141942 | 185 | 14 | 10 | 7693 | 22253 | 19 | 376 | 24 | 2258 | 1 | 65 | 107 | 623 | 34109 | 396223 | 52 | 1462 | 2359 | 55949 | 424 | 520 | 165 | 249 | 261 | 4 | 39 | 11 |
77 rows × 184 columns
pd.set_option('display.max_columns', None)
confirmed_country.describe(include='all')
| Country/Region | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | Austria | Azerbaijan | Bahamas | Bahrain | Bangladesh | Barbados | Belarus | Belgium | Belize | Benin | Bhutan | Bolivia | Bosnia and Herzegovina | Botswana | Brazil | Brunei | Bulgaria | Burkina Faso | Burma | Burundi | Cabo Verde | Cambodia | Cameroon | Canada | Central African Republic | Chad | Chile | China | Colombia | Congo (Brazzaville) | Congo (Kinshasa) | Costa Rica | Cote d'Ivoire | Croatia | Cuba | Cyprus | Czechia | Denmark | Diamond Princess | Djibouti | Dominica | Dominican Republic | Ecuador | Egypt | El Salvador | Equatorial Guinea | Eritrea | Estonia | Eswatini | Ethiopia | Fiji | Finland | France | Gabon | Gambia | Georgia | Germany | Ghana | Greece | Grenada | Guatemala | Guinea | Guinea-Bissau | Guyana | Haiti | Holy See | Honduras | Hungary | Iceland | India | Indonesia | Iran | Iraq | Ireland | Israel | Italy | Jamaica | Japan | Jordan | Kazakhstan | Kenya | Korea, South | Kosovo | Kuwait | Kyrgyzstan | Laos | Latvia | Lebanon | Liberia | Libya | Liechtenstein | Lithuania | Luxembourg | MS Zaandam | Madagascar | Malawi | Malaysia | Maldives | Mali | Malta | Mauritania | Mauritius | Mexico | Moldova | Monaco | Mongolia | Montenegro | Morocco | Mozambique | Namibia | Nepal | Netherlands | New Zealand | Nicaragua | Niger | Nigeria | North Macedonia | Norway | Oman | Pakistan | Panama | Papua New Guinea | Paraguay | Peru | Philippines | Poland | Portugal | Qatar | Romania | Russia | Rwanda | Saint Kitts and Nevis | Saint Lucia | Saint Vincent and the Grenadines | San Marino | Sao Tome and Principe | Saudi Arabia | Senegal | Serbia | Seychelles | Sierra Leone | Singapore | Slovakia | Slovenia | Somalia | South Africa | South Sudan | Spain | Sri Lanka | Sudan | Suriname | Sweden | Switzerland | Syria | Taiwan* | Tanzania | Thailand | Timor-Leste | Togo | Trinidad and Tobago | Tunisia | Turkey | US | Uganda | Ukraine | United Arab Emirates | United Kingdom | Uruguay | Uzbekistan | Venezuela | Vietnam | West Bank and Gaza | Western Sahara | Zambia | Zimbabwe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 |
| mean | 44.857143 | 58.779221 | 172.649351 | 78.974026 | 1.675325 | 2.025974 | 208.636364 | 121.389610 | 966.038961 | 2168.220779 | 72.636364 | 3.714286 | 165.857143 | 15.090909 | 8.012987 | 62.961039 | 2808.402597 | 0.662338 | 2.597403 | 1.038961 | 22.402597 | 92.376623 | 0.532468 | 1412.376623 | 34.103896 | 92.207792 | 54.337662 | 2.545455 | 0.285714 | 1.233766 | 26.610390 | 60.207792 | 1931.311688 | 1.129870 | 1.233766 | 602.571429 | 61878.272727 | 206.168831 | 4.116883 | 21.662338 | 78.090909 | 36.766234 | 185.545455 | 40.103896 | 64.831169 | 731.675325 | 811.779221 | 480.883117 | 6.961039 | 2.246753 | 224.792208 | 524.064935 | 192.207792 | 7.012987 | 3.116883 | 3.168831 | 186.285714 | 1.818182 | 6.142857 | 1.545455 | 341.441558 | 12822.610390 | 3.142857 | 0.818182 | 30.987013 | 16231.623377 | 34.675325 | 324.363636 | 1.701299 | 9.584416 | 9.909091 | 1.896104 | 4.064935 | 3.064935 | 1.376623 | 33.363636 | 106.714286 | 276.025974 | 444.220779 | 350.883117 | 11894.363636 | 164.688312 | 721.415584 | 1136.311688 | 26992.792208 | 9.766234 | 669.610390 | 60.753247 | 73.883117 | 16.636364 | 4259.935065 | 19.727273 | 101.870130 | 21.805195 | 1.649351 | 88.389610 | 114.402597 | 1.441558 | 1.818182 | 15.857143 | 115.012987 | 467.194805 | 0.922078 | 9.987013 | 0.350649 | 681.311688 | 5.480519 | 5.207792 | 42.428571 | 1.142857 | 29.766234 | 249.207792 | 97.194805 | 11.792208 | 3.246753 | 26.389610 | 131.883117 | 1.636364 | 2.610390 | 1.883117 | 2852.441558 | 133.337662 | 0.922078 | 16.493506 | 30.532468 | 75.948052 | 1169.337662 | 47.948052 | 475.168831 | 244.831169 | 0.272727 | 15.740260 | 280.207792 | 431.961039 | 546.714286 | 1559.272727 | 259.350649 | 507.454545 | 606.727273 | 16.233766 | 1.181818 | 2.090909 | 0.688312 | 68.311688 | 0.103896 | 362.220779 | 38.181818 | 230.532468 | 2.376623 | 0.376623 | 281.051948 | 88.142857 | 199.155844 | 0.974026 | 275.259740 | 0.051948 | 20967.662338 | 34.623377 | 1.636364 | 2.051948 | 1152.454545 | 3855.233766 | 2.168831 | 91.272727 | 4.064935 | 374.597403 | 0.220779 | 7.805195 | 18.935065 | 80.454545 | 2971.948052 | 41931.961039 | 7.168831 | 144.688312 | 216.051948 | 6072.844156 | 70.350649 | 42.337662 | 30.935065 | 59.649351 | 36.532468 | 0.155844 | 6.025974 | 1.532468 |
| std | 96.349516 | 105.241405 | 366.068974 | 154.181538 | 3.729123 | 4.301083 | 427.103461 | 235.506629 | 1787.144278 | 3922.499588 | 159.561151 | 8.063657 | 229.753169 | 29.775114 | 16.413644 | 154.117474 | 5673.749942 | 1.535633 | 5.788509 | 1.584809 | 46.622295 | 189.210760 | 1.518174 | 3127.909357 | 51.186600 | 164.768285 | 106.381712 | 6.309027 | 0.856202 | 2.333374 | 42.747778 | 155.541443 | 4233.173254 | 2.154253 | 2.655164 | 1273.367587 | 29056.328977 | 430.525517 | 10.131989 | 45.138270 | 142.085888 | 81.235022 | 358.443035 | 91.019313 | 127.137659 | 1386.498528 | 1353.824637 | 307.498779 | 18.386524 | 4.718820 | 494.007760 | 1064.491296 | 344.350355 | 17.088387 | 5.443427 | 7.834476 | 323.878505 | 3.370644 | 12.059845 | 3.354280 | 600.881797 | 25646.732643 | 6.744811 | 1.466533 | 50.706714 | 30258.667724 | 71.350219 | 537.939977 | 3.755925 | 18.587879 | 29.666205 | 5.524073 | 7.788888 | 6.471268 | 2.322878 | 75.863088 | 211.377615 | 467.988584 | 1048.533294 | 680.517546 | 17903.433811 | 283.762422 | 1449.580296 | 2422.764734 | 42182.079927 | 16.860805 | 922.346367 | 110.526272 | 165.468472 | 39.069408 | 4216.801842 | 45.489917 | 156.118237 | 49.664410 | 3.644629 | 163.182748 | 179.527396 | 3.109804 | 4.781343 | 26.753118 | 236.738669 | 885.460630 | 2.609528 | 22.308538 | 1.305517 | 1140.920384 | 7.284826 | 13.047825 | 74.099760 | 2.030778 | 65.166567 | 542.709512 | 226.408750 | 21.981055 | 5.226843 | 58.882612 | 284.983378 | 3.410159 | 4.801743 | 2.139688 | 5355.346987 | 294.984505 | 1.797412 | 52.135104 | 65.877395 | 151.462597 | 1856.982730 | 87.639948 | 945.675398 | 516.622009 | 0.503576 | 30.247402 | 614.361272 | 939.844408 | 1143.532383 | 3255.235611 | 440.944553 | 1075.276030 | 1508.093591 | 31.380473 | 2.932249 | 4.249402 | 1.648442 | 94.647443 | 0.640403 | 707.483766 | 69.206732 | 524.175031 | 3.762834 | 1.287991 | 377.252808 | 155.031806 | 322.872228 | 2.025976 | 534.143964 | 0.276067 | 39780.792713 | 56.595062 | 3.248113 | 3.586877 | 2000.654844 | 6749.319301 | 5.094896 | 116.505211 | 7.354443 | 655.328709 | 0.417492 | 15.066553 | 34.769291 | 166.073188 | 7446.266028 | 93967.244676 | 15.737487 | 349.981814 | 474.462075 | 13196.987195 | 129.867520 | 100.384997 | 53.837557 | 76.847896 | 63.904448 | 0.779084 | 12.890198 | 3.076158 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 548.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 15.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 7.000000 | 0.000000 | 0.000000 | 0.000000 | 42354.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 135.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 11.000000 | 0.000000 | 0.000000 | 0.000000 | 14.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | 0.000000 | 26.000000 | 0.000000 | 0.000000 | 0.000000 | 27.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 18.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 45.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 18.000000 | 0.000000 | 32.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 11.000000 | 0.000000 | 0.000000 | 8.000000 | 8.000000 | 0.000000 | 0.000000 | 0.000000 | 14.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 50% | 1.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 25.000000 | 9.000000 | 0.000000 | 0.000000 | 41.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 20.000000 | 0.000000 | 0.000000 | 0.000000 | 79356.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 6.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | 705.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | 100.000000 | 0.000000 | 0.000000 | 1.000000 | 79.000000 | 0.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 3.000000 | 0.000000 | 593.000000 | 13.000000 | 1.000000 | 7.000000 | 1128.000000 | 0.000000 | 241.000000 | 0.000000 | 0.000000 | 0.000000 | 3150.000000 | 0.000000 | 45.000000 | 0.000000 | 0.000000 | 0.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 25.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 4.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 6.000000 | 1.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 15.000000 | 6.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 3.000000 | 0.000000 | 0.000000 | 1.000000 | 3.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 102.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 45.000000 | 1.000000 | 0.000000 | 0.000000 | 12.000000 | 18.000000 | 0.000000 | 39.000000 | 0.000000 | 42.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 68.000000 | 0.000000 | 0.000000 | 21.000000 | 23.000000 | 0.000000 | 0.000000 | 0.000000 | 16.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 |
| 75% | 22.000000 | 64.000000 | 87.000000 | 53.000000 | 0.000000 | 1.000000 | 97.000000 | 115.000000 | 681.000000 | 2013.000000 | 44.000000 | 3.000000 | 278.000000 | 17.000000 | 5.000000 | 51.000000 | 1795.000000 | 0.000000 | 2.000000 | 1.000000 | 12.000000 | 63.000000 | 0.000000 | 621.000000 | 75.000000 | 94.000000 | 33.000000 | 0.000000 | 0.000000 | 0.000000 | 37.000000 | 13.000000 | 800.000000 | 1.000000 | 1.000000 | 238.000000 | 81156.000000 | 102.000000 | 3.000000 | 14.000000 | 69.000000 | 9.000000 | 105.000000 | 11.000000 | 67.000000 | 694.000000 | 1225.000000 | 712.000000 | 1.000000 | 0.000000 | 34.000000 | 199.000000 | 256.000000 | 1.000000 | 6.000000 | 0.000000 | 267.000000 | 1.000000 | 6.000000 | 1.000000 | 400.000000 | 10970.000000 | 1.000000 | 1.000000 | 40.000000 | 15320.000000 | 11.000000 | 418.000000 | 0.000000 | 9.000000 | 1.000000 | 0.000000 | 5.000000 | 0.000000 | 1.000000 | 12.000000 | 73.000000 | 330.000000 | 194.000000 | 311.000000 | 18407.000000 | 192.000000 | 557.000000 | 427.000000 | 41035.000000 | 15.000000 | 924.000000 | 69.000000 | 44.000000 | 7.000000 | 8565.000000 | 0.000000 | 148.000000 | 3.000000 | 0.000000 | 86.000000 | 157.000000 | 2.000000 | 0.000000 | 28.000000 | 36.000000 | 335.000000 | 0.000000 | 0.000000 | 0.000000 | 900.000000 | 13.000000 | 0.000000 | 53.000000 | 2.000000 | 3.000000 | 118.000000 | 49.000000 | 7.000000 | 6.000000 | 3.000000 | 63.000000 | 0.000000 | 3.000000 | 1.000000 | 2467.000000 | 28.000000 | 1.000000 | 0.000000 | 8.000000 | 48.000000 | 1746.000000 | 48.000000 | 454.000000 | 109.000000 | 0.000000 | 11.000000 | 234.000000 | 217.000000 | 355.000000 | 785.000000 | 460.000000 | 277.000000 | 199.000000 | 8.000000 | 0.000000 | 2.000000 | 1.000000 | 119.000000 | 0.000000 | 274.000000 | 31.000000 | 103.000000 | 6.000000 | 0.000000 | 345.000000 | 123.000000 | 286.000000 | 1.000000 | 150.000000 | 0.000000 | 17963.000000 | 60.000000 | 2.000000 | 1.000000 | 1439.000000 | 4075.000000 | 0.000000 | 108.000000 | 6.000000 | 272.000000 | 0.000000 | 1.000000 | 9.000000 | 39.000000 | 192.000000 | 13747.000000 | 0.000000 | 16.000000 | 140.000000 | 2716.000000 | 79.000000 | 23.000000 | 42.000000 | 85.000000 | 44.000000 | 0.000000 | 2.000000 | 0.000000 |
| max | 423.000000 | 383.000000 | 1468.000000 | 545.000000 | 17.000000 | 19.000000 | 1628.000000 | 853.000000 | 5895.000000 | 12639.000000 | 717.000000 | 33.000000 | 811.000000 | 164.000000 | 63.000000 | 861.000000 | 22194.000000 | 7.000000 | 26.000000 | 5.000000 | 194.000000 | 764.000000 | 6.000000 | 14034.000000 | 135.000000 | 577.000000 | 384.000000 | 22.000000 | 3.000000 | 7.000000 | 115.000000 | 658.000000 | 17872.000000 | 8.000000 | 10.000000 | 5116.000000 | 82718.000000 | 1780.000000 | 45.000000 | 180.000000 | 483.000000 | 349.000000 | 1282.000000 | 396.000000 | 494.000000 | 5017.000000 | 5266.000000 | 712.000000 | 90.000000 | 15.000000 | 1956.000000 | 3747.000000 | 1450.000000 | 78.000000 | 16.000000 | 31.000000 | 1149.000000 | 10.000000 | 52.000000 | 15.000000 | 2308.000000 | 110065.000000 | 30.000000 | 4.000000 | 196.000000 | 107663.000000 | 287.000000 | 1832.000000 | 12.000000 | 77.000000 | 144.000000 | 33.000000 | 33.000000 | 25.000000 | 7.000000 | 305.000000 | 817.000000 | 1586.000000 | 5311.000000 | 2738.000000 | 62589.000000 | 1122.000000 | 5709.000000 | 9248.000000 | 135586.000000 | 63.000000 | 3906.000000 | 353.000000 | 697.000000 | 172.000000 | 10331.000000 | 170.000000 | 743.000000 | 228.000000 | 14.000000 | 548.000000 | 548.000000 | 14.000000 | 20.000000 | 78.000000 | 880.000000 | 2970.000000 | 9.000000 | 88.000000 | 8.000000 | 3963.000000 | 19.000000 | 56.000000 | 293.000000 | 6.000000 | 268.000000 | 2439.000000 | 1056.000000 | 79.000000 | 15.000000 | 241.000000 | 1184.000000 | 10.000000 | 16.000000 | 9.000000 | 19709.000000 | 1160.000000 | 6.000000 | 278.000000 | 254.000000 | 599.000000 | 6086.000000 | 371.000000 | 4035.000000 | 2100.000000 | 2.000000 | 115.000000 | 2954.000000 | 3764.000000 | 4848.000000 | 12442.000000 | 2057.000000 | 4417.000000 | 7497.000000 | 105.000000 | 11.000000 | 14.000000 | 8.000000 | 279.000000 | 4.000000 | 2795.000000 | 237.000000 | 2447.000000 | 11.000000 | 6.000000 | 1481.000000 | 581.000000 | 1059.000000 | 8.000000 | 1749.000000 | 2.000000 | 141942.000000 | 185.000000 | 14.000000 | 10.000000 | 7693.000000 | 22253.000000 | 19.000000 | 376.000000 | 24.000000 | 2258.000000 | 1.000000 | 65.000000 | 107.000000 | 623.000000 | 34109.000000 | 396223.000000 | 52.000000 | 1462.000000 | 2359.000000 | 55949.000000 | 424.000000 | 520.000000 | 165.000000 | 249.000000 | 261.000000 | 4.000000 | 39.000000 | 11.000000 |
print("Kasus terkecil namun paling tinggi: ", confirmed_country.min().max())
print("Nilai maximum namun yang paling rendah dari lainnya: ",confirmed_country.max().min())
print("Nilai maximum namun yang paling tinggi dari lainnya: ",confirmed_country.max().max())
Kasus terkecil namun paling tinggi: 548 Nilai maximum namun yang paling rendah dari lainnya: 1 Nilai maximum namun yang paling tinggi dari lainnya: 396223
confirmed_country.info()
<class 'pandas.core.frame.DataFrame'> DatetimeIndex: 77 entries, 2020-01-22 to 2020-04-07 Columns: 184 entries, Afghanistan to Zimbabwe dtypes: int64(184) memory usage: 111.3 KB
Berdasarka tabel diatas dapat dilihat bahwa pada data set confirmed_country memiliki 77 pengamatan yakni tanggal yang berurutan mulai dari 22 Januari 2020 sampai dengan 7 April 2020. Pengamatan terdiri dari 184 kolom, dalam hal ini terdapat 184 negara mulai dari Afganistan hingga Zimbabwe. Seluruh sel diisi dengan bilangan bulat yang menyatakan jumlah kumlatif total pasien yang terkonfirmasi per harinya. Data set confirmed_country memiliki data yang lengkap, hal ini dibuktikan melalui method .info yang menyatakan bahwa tidak ditemukan missing value pada data set ini.
Berdasarkan tabel-tabel diatas didapatkan juga informasi sebagai berikut:
Anda sudah memiliki sebuah dataframe yang berisi kasus terkonfirmasi COVID-19 yang terindeks berdasarkan waktu. Selanjutnya, visualisasikan data jumlah kasus terkonfirmasi di negara-negara berikut (Prancis, Spanyol, Cina, AS, Italia, dan Australia). Berikan judul, labels, dan spesifikasi (ukuran, warna, ketebalan, dll) yang sesuai, sehingga plot yang dihasilkan rapi, menarik, dan mudah dipahami.
enamnegara = confirmed_country[["France", "Spain", "China", "US", "Italy", "Australia"]]
enamnegara = pd.DataFrame(enamnegara)
enamnegara
| Country/Region | France | Spain | China | US | Italy | Australia |
|---|---|---|---|---|---|---|
| Date | ||||||
| 2020-01-22 | 0 | 0 | 548 | 1 | 0 | 0 |
| 2020-01-23 | 0 | 0 | 643 | 1 | 0 | 0 |
| 2020-01-24 | 2 | 0 | 920 | 2 | 0 | 0 |
| 2020-01-25 | 3 | 0 | 1406 | 2 | 0 | 0 |
| 2020-01-26 | 3 | 0 | 2075 | 5 | 0 | 4 |
| ... | ... | ... | ... | ... | ... | ... |
| 2020-04-03 | 65202 | 119199 | 82511 | 275586 | 119827 | 5330 |
| 2020-04-04 | 90848 | 126168 | 82543 | 308850 | 124632 | 5550 |
| 2020-04-05 | 93773 | 131646 | 82602 | 337072 | 128948 | 5687 |
| 2020-04-06 | 98963 | 136675 | 82665 | 366667 | 132547 | 5797 |
| 2020-04-07 | 110065 | 141942 | 82718 | 396223 | 135586 | 5895 |
77 rows × 6 columns
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
fig, axes = plt.subplots(nrows=6, ncols=1)
for i, c in enumerate(enamnegara.columns):
enamnegara[c].plot(kind='line', ax=axes[i], title=c, xlabel="Tanggal", ylabel="Total Kasus Terkonfirmasi", legend=c, linewidth=4,fontsize=16)
plt.subplots_adjust(hspace = 1.0)
plt.gcf().set_size_inches(10, 28)
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
enamnegara.plot(figsize=(18,10), linewidth=3, fontsize=20)
plt.title("Kasus Terkonfirmasi COVID-19 di Prancis, Spanyol, Cina, As, Itali, dan Autralia", fontsize=20)
Text(0.5, 1.0, 'Kasus Terkonfirmasi COVID-19 di Prancis, Spanyol, Cina, As, Itali, dan Autralia')
negara = {'Negara':pd.Series(["France", "Spain", "China","US", "Italy","Australia"]),
'Total Terkonfirmasi per 7 April 2020': pd.Series([110065,141942,82718,396223,135586,5895])}
datakonfirmasi = pd.DataFrame(negara)
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
datakonfirmasi.plot.bar(x="Negara", y="Total Terkonfirmasi per 7 April 2020",rot=0,figsize=(10,7), ylabel = "Jumlah Kasus", edgecolor='k', fontsize=15)
plt.title("Jumlah Akhir Kasus Terkonfirmasi di 6 Negara",fontsize=17)
Text(0.5, 1.0, 'Jumlah Akhir Kasus Terkonfirmasi di 6 Negara')
Berdasarkan visualisasi di atas didapatkan informasi didapatkan informasi sebagai berikut:
Selain informasi kasus terkonfirmasi, anda juga perlu mendapatkan informasi mengenai kasus kematian COVID-19. Tampilkan beberapa baris teratas/terbawah beserta informasi dari dataframe raw_data_deaths
raw_data_deaths.head()
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | 1/28/20 | 1/29/20 | 1/30/20 | 1/31/20 | 2/1/20 | 2/2/20 | 2/3/20 | 2/4/20 | 2/5/20 | 2/6/20 | 2/7/20 | 2/8/20 | 2/9/20 | 2/10/20 | 2/11/20 | 2/12/20 | 2/13/20 | 2/14/20 | 2/15/20 | 2/16/20 | 2/17/20 | 2/18/20 | 2/19/20 | 2/20/20 | 2/21/20 | 2/22/20 | 2/23/20 | 2/24/20 | 2/25/20 | 2/26/20 | 2/27/20 | 2/28/20 | 2/29/20 | 3/1/20 | 3/2/20 | 3/3/20 | 3/4/20 | 3/5/20 | 3/6/20 | 3/7/20 | 3/8/20 | 3/9/20 | 3/10/20 | 3/11/20 | 3/12/20 | 3/13/20 | 3/14/20 | 3/15/20 | 3/16/20 | 3/17/20 | 3/18/20 | 3/19/20 | 3/20/20 | 3/21/20 | 3/22/20 | 3/23/20 | 3/24/20 | 3/25/20 | 3/26/20 | 3/27/20 | 3/28/20 | 3/29/20 | 3/30/20 | 3/31/20 | 4/1/20 | 4/2/20 | 4/3/20 | 4/4/20 | 4/5/20 | 4/6/20 | 4/7/20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | NaN | Afghanistan | 33.0000 | 65.0000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 2 | 4 | 4 | 4 | 4 | 4 | 4 | 4 | 6 | 6 | 7 | 7 | 11 | 14 |
| 1 | NaN | Albania | 41.1533 | 20.1683 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 4 | 5 | 5 | 6 | 8 | 10 | 10 | 11 | 15 | 15 | 16 | 17 | 20 | 20 | 21 | 22 |
| 2 | NaN | Algeria | 28.0339 | 1.6596 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 2 | 3 | 4 | 4 | 4 | 7 | 9 | 11 | 15 | 17 | 17 | 19 | 21 | 25 | 26 | 29 | 31 | 35 | 44 | 58 | 86 | 105 | 130 | 152 | 173 | 193 |
| 3 | NaN | Andorra | 42.5063 | 1.5218 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 3 | 3 | 3 | 6 | 8 | 12 | 14 | 15 | 16 | 17 | 18 | 21 | 22 |
| 4 | NaN | Angola | -11.2027 | 17.8739 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |
raw_data_deaths.tail()
| Province/State | Country/Region | Lat | Long | 1/22/20 | 1/23/20 | 1/24/20 | 1/25/20 | 1/26/20 | 1/27/20 | 1/28/20 | 1/29/20 | 1/30/20 | 1/31/20 | 2/1/20 | 2/2/20 | 2/3/20 | 2/4/20 | 2/5/20 | 2/6/20 | 2/7/20 | 2/8/20 | 2/9/20 | 2/10/20 | 2/11/20 | 2/12/20 | 2/13/20 | 2/14/20 | 2/15/20 | 2/16/20 | 2/17/20 | 2/18/20 | 2/19/20 | 2/20/20 | 2/21/20 | 2/22/20 | 2/23/20 | 2/24/20 | 2/25/20 | 2/26/20 | 2/27/20 | 2/28/20 | 2/29/20 | 3/1/20 | 3/2/20 | 3/3/20 | 3/4/20 | 3/5/20 | 3/6/20 | 3/7/20 | 3/8/20 | 3/9/20 | 3/10/20 | 3/11/20 | 3/12/20 | 3/13/20 | 3/14/20 | 3/15/20 | 3/16/20 | 3/17/20 | 3/18/20 | 3/19/20 | 3/20/20 | 3/21/20 | 3/22/20 | 3/23/20 | 3/24/20 | 3/25/20 | 3/26/20 | 3/27/20 | 3/28/20 | 3/29/20 | 3/30/20 | 3/31/20 | 4/1/20 | 4/2/20 | 4/3/20 | 4/4/20 | 4/5/20 | 4/6/20 | 4/7/20 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 258 | Falkland Islands (Malvinas) | United Kingdom | -51.79630 | -59.523600 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 259 | Saint Pierre and Miquelon | France | 46.88520 | -56.315900 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 260 | NaN | South Sudan | 6.87700 | 31.307000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 261 | NaN | Western Sahara | 24.21550 | -12.885800 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 262 | NaN | Sao Tome and Principe | 0.18636 | 6.613081 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
raw_data_deaths.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 263 entries, 0 to 262 Data columns (total 81 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Province/State 82 non-null object 1 Country/Region 263 non-null object 2 Lat 263 non-null float64 3 Long 263 non-null float64 4 1/22/20 263 non-null int64 5 1/23/20 263 non-null int64 6 1/24/20 263 non-null int64 7 1/25/20 263 non-null int64 8 1/26/20 263 non-null int64 9 1/27/20 263 non-null int64 10 1/28/20 263 non-null int64 11 1/29/20 263 non-null int64 12 1/30/20 263 non-null int64 13 1/31/20 263 non-null int64 14 2/1/20 263 non-null int64 15 2/2/20 263 non-null int64 16 2/3/20 263 non-null int64 17 2/4/20 263 non-null int64 18 2/5/20 263 non-null int64 19 2/6/20 263 non-null int64 20 2/7/20 263 non-null int64 21 2/8/20 263 non-null int64 22 2/9/20 263 non-null int64 23 2/10/20 263 non-null int64 24 2/11/20 263 non-null int64 25 2/12/20 263 non-null int64 26 2/13/20 263 non-null int64 27 2/14/20 263 non-null int64 28 2/15/20 263 non-null int64 29 2/16/20 263 non-null int64 30 2/17/20 263 non-null int64 31 2/18/20 263 non-null int64 32 2/19/20 263 non-null int64 33 2/20/20 263 non-null int64 34 2/21/20 263 non-null int64 35 2/22/20 263 non-null int64 36 2/23/20 263 non-null int64 37 2/24/20 263 non-null int64 38 2/25/20 263 non-null int64 39 2/26/20 263 non-null int64 40 2/27/20 263 non-null int64 41 2/28/20 263 non-null int64 42 2/29/20 263 non-null int64 43 3/1/20 263 non-null int64 44 3/2/20 263 non-null int64 45 3/3/20 263 non-null int64 46 3/4/20 263 non-null int64 47 3/5/20 263 non-null int64 48 3/6/20 263 non-null int64 49 3/7/20 263 non-null int64 50 3/8/20 263 non-null int64 51 3/9/20 263 non-null int64 52 3/10/20 263 non-null int64 53 3/11/20 263 non-null int64 54 3/12/20 263 non-null int64 55 3/13/20 263 non-null int64 56 3/14/20 263 non-null int64 57 3/15/20 263 non-null int64 58 3/16/20 263 non-null int64 59 3/17/20 263 non-null int64 60 3/18/20 263 non-null int64 61 3/19/20 263 non-null int64 62 3/20/20 263 non-null int64 63 3/21/20 263 non-null int64 64 3/22/20 263 non-null int64 65 3/23/20 263 non-null int64 66 3/24/20 263 non-null int64 67 3/25/20 263 non-null int64 68 3/26/20 263 non-null int64 69 3/27/20 263 non-null int64 70 3/28/20 263 non-null int64 71 3/29/20 263 non-null int64 72 3/30/20 263 non-null int64 73 3/31/20 263 non-null int64 74 4/1/20 263 non-null int64 75 4/2/20 263 non-null int64 76 4/3/20 263 non-null int64 77 4/4/20 263 non-null int64 78 4/5/20 263 non-null int64 79 4/6/20 263 non-null int64 80 4/7/20 263 non-null int64 dtypes: float64(2), int64(77), object(2) memory usage: 166.6+ KB
Berdasarka tabel diatas dapat dilihat bahwa pada data set raw_data_deaths memiliki 77 pengamatan yakni tanggal yang berurutan mulai dari 22 Januari 2020 sampai dengan 7 April 2020. Pengamatan terdiri dari 263 negara mulai dari Afganistan hingga Zimbabwe. Seluruh sel diisi dengan bilangan bulat yang menyatakan jumlah kumlatif total orang yang meninggal per harinya, bilangan desimal untuk menunjukan lat dan long sebuah negara, serta string untuk menunjukan nama kota/provinsi dan nama negara. Data set "raw_data_deaths" memiliki data yang yang hilang yaitu pada kolom Province/State, namun hal ini tidak terlalu penting dikarenakan pengamatan hanya memusatkan pada variabel Country/Region. Adanya missing value dibuktikan melalui method .info yang menyatakan bahwa terdapat nilai non null pada kolom Province/State sebanyal 83 sel, sedangkan untuk tanggal perharinya semua sel terisi penuh.
Selanjutnya, tampilkan beberapa baris teratas/terbawah data kasus kematian COVID-19 di setiap negara yang terindeks berdasarkan waktu(date/time) bukan berdasarkan Country/Region.
deaths = raw_data_deaths.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
value_vars=raw_data_deaths.iloc[:,4:],
var_name='Date', value_name='Total Pasien Meninggal')
deaths["Date"] = pd.to_datetime(deaths["Date"])
deaths = deaths.drop(["Province/State","Lat","Long"], axis = 1)
deaths = deaths.groupby(['Date','Country/Region'],as_index=False).agg({'Total Pasien Meninggal': 'sum'})
deaths = deaths.pivot_table(index=['Date'], columns='Country/Region',
values='Total Pasien Meninggal', aggfunc='first').reset_index()
deaths = deaths.set_index(['Date'])
pd.set_option('display.max_columns', None)
deaths.head()
| Country/Region | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | Austria | Azerbaijan | Bahamas | Bahrain | Bangladesh | Barbados | Belarus | Belgium | Belize | Benin | Bhutan | Bolivia | Bosnia and Herzegovina | Botswana | Brazil | Brunei | Bulgaria | Burkina Faso | Burma | Burundi | Cabo Verde | Cambodia | Cameroon | Canada | Central African Republic | Chad | Chile | China | Colombia | Congo (Brazzaville) | Congo (Kinshasa) | Costa Rica | Cote d'Ivoire | Croatia | Cuba | Cyprus | Czechia | Denmark | Diamond Princess | Djibouti | Dominica | Dominican Republic | Ecuador | Egypt | El Salvador | Equatorial Guinea | Eritrea | Estonia | Eswatini | Ethiopia | Fiji | Finland | France | Gabon | Gambia | Georgia | Germany | Ghana | Greece | Grenada | Guatemala | Guinea | Guinea-Bissau | Guyana | Haiti | Holy See | Honduras | Hungary | Iceland | India | Indonesia | Iran | Iraq | Ireland | Israel | Italy | Jamaica | Japan | Jordan | Kazakhstan | Kenya | Korea, South | Kosovo | Kuwait | Kyrgyzstan | Laos | Latvia | Lebanon | Liberia | Libya | Liechtenstein | Lithuania | Luxembourg | MS Zaandam | Madagascar | Malawi | Malaysia | Maldives | Mali | Malta | Mauritania | Mauritius | Mexico | Moldova | Monaco | Mongolia | Montenegro | Morocco | Mozambique | Namibia | Nepal | Netherlands | New Zealand | Nicaragua | Niger | Nigeria | North Macedonia | Norway | Oman | Pakistan | Panama | Papua New Guinea | Paraguay | Peru | Philippines | Poland | Portugal | Qatar | Romania | Russia | Rwanda | Saint Kitts and Nevis | Saint Lucia | Saint Vincent and the Grenadines | San Marino | Sao Tome and Principe | Saudi Arabia | Senegal | Serbia | Seychelles | Sierra Leone | Singapore | Slovakia | Slovenia | Somalia | South Africa | South Sudan | Spain | Sri Lanka | Sudan | Suriname | Sweden | Switzerland | Syria | Taiwan* | Tanzania | Thailand | Timor-Leste | Togo | Trinidad and Tobago | Tunisia | Turkey | US | Uganda | Ukraine | United Arab Emirates | United Kingdom | Uruguay | Uzbekistan | Venezuela | Vietnam | West Bank and Gaza | Western Sahara | Zambia | Zimbabwe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 2020-01-22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 42 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 56 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
pd.set_option('display.max_columns', None)
deaths.tail()
| Country/Region | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | Austria | Azerbaijan | Bahamas | Bahrain | Bangladesh | Barbados | Belarus | Belgium | Belize | Benin | Bhutan | Bolivia | Bosnia and Herzegovina | Botswana | Brazil | Brunei | Bulgaria | Burkina Faso | Burma | Burundi | Cabo Verde | Cambodia | Cameroon | Canada | Central African Republic | Chad | Chile | China | Colombia | Congo (Brazzaville) | Congo (Kinshasa) | Costa Rica | Cote d'Ivoire | Croatia | Cuba | Cyprus | Czechia | Denmark | Diamond Princess | Djibouti | Dominica | Dominican Republic | Ecuador | Egypt | El Salvador | Equatorial Guinea | Eritrea | Estonia | Eswatini | Ethiopia | Fiji | Finland | France | Gabon | Gambia | Georgia | Germany | Ghana | Greece | Grenada | Guatemala | Guinea | Guinea-Bissau | Guyana | Haiti | Holy See | Honduras | Hungary | Iceland | India | Indonesia | Iran | Iraq | Ireland | Israel | Italy | Jamaica | Japan | Jordan | Kazakhstan | Kenya | Korea, South | Kosovo | Kuwait | Kyrgyzstan | Laos | Latvia | Lebanon | Liberia | Libya | Liechtenstein | Lithuania | Luxembourg | MS Zaandam | Madagascar | Malawi | Malaysia | Maldives | Mali | Malta | Mauritania | Mauritius | Mexico | Moldova | Monaco | Mongolia | Montenegro | Morocco | Mozambique | Namibia | Nepal | Netherlands | New Zealand | Nicaragua | Niger | Nigeria | North Macedonia | Norway | Oman | Pakistan | Panama | Papua New Guinea | Paraguay | Peru | Philippines | Poland | Portugal | Qatar | Romania | Russia | Rwanda | Saint Kitts and Nevis | Saint Lucia | Saint Vincent and the Grenadines | San Marino | Sao Tome and Principe | Saudi Arabia | Senegal | Serbia | Seychelles | Sierra Leone | Singapore | Slovakia | Slovenia | Somalia | South Africa | South Sudan | Spain | Sri Lanka | Sudan | Suriname | Sweden | Switzerland | Syria | Taiwan* | Tanzania | Thailand | Timor-Leste | Togo | Trinidad and Tobago | Tunisia | Turkey | US | Uganda | Ukraine | United Arab Emirates | United Kingdom | Uruguay | Uzbekistan | Venezuela | Vietnam | West Bank and Gaza | Western Sahara | Zambia | Zimbabwe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 2020-04-03 | 6 | 17 | 105 | 16 | 2 | 0 | 39 | 7 | 28 | 168 | 5 | 1 | 4 | 6 | 0 | 4 | 1143 | 0 | 0 | 0 | 9 | 17 | 1 | 359 | 1 | 14 | 16 | 1 | 0 | 1 | 0 | 8 | 179 | 0 | 0 | 22 | 3326 | 25 | 2 | 13 | 2 | 1 | 8 | 6 | 11 | 53 | 139 | 11 | 0 | 0 | 68 | 145 | 66 | 2 | 0 | 0 | 12 | 0 | 0 | 0 | 20 | 6520 | 1 | 1 | 0 | 1275 | 5 | 63 | 0 | 1 | 0 | 0 | 4 | 0 | 0 | 15 | 26 | 4 | 72 | 181 | 3294 | 54 | 120 | 40 | 14681 | 3 | 63 | 5 | 6 | 4 | 174 | 1 | 0 | 1 | 0 | 1 | 17 | 0 | 1 | 0 | 9 | 31 | 2 | 0 | 0 | 53 | 0 | 3 | 0 | 1 | 7 | 50 | 8 | 1 | 0 | 2 | 48 | 0 | 0 | 0 | 1490 | 1 | 1 | 5 | 4 | 12 | 59 | 1 | 40 | 37 | 0 | 3 | 61 | 136 | 71 | 246 | 3 | 133 | 34 | 0 | 0 | 0 | 0 | 30 | 0 | 25 | 1 | 39 | 0 | 0 | 5 | 1 | 20 | 0 | 9 | 0 | 11198 | 4 | 2 | 1 | 358 | 591 | 2 | 5 | 1 | 19 | 0 | 3 | 6 | 18 | 425 | 7087 | 0 | 27 | 9 | 3611 | 4 | 2 | 7 | 0 | 1 | 0 | 1 | 1 |
| 2020-04-04 | 7 | 20 | 130 | 17 | 2 | 0 | 43 | 7 | 30 | 186 | 5 | 4 | 4 | 8 | 0 | 5 | 1283 | 0 | 0 | 0 | 10 | 21 | 1 | 445 | 1 | 17 | 16 | 1 | 0 | 1 | 0 | 9 | 218 | 0 | 0 | 27 | 3330 | 32 | 2 | 18 | 2 | 1 | 12 | 6 | 11 | 59 | 161 | 11 | 0 | 0 | 68 | 172 | 71 | 3 | 0 | 0 | 13 | 0 | 0 | 0 | 25 | 7574 | 1 | 1 | 1 | 1444 | 5 | 68 | 0 | 2 | 0 | 0 | 4 | 0 | 0 | 15 | 32 | 4 | 86 | 191 | 3452 | 56 | 137 | 44 | 15362 | 3 | 77 | 5 | 5 | 4 | 177 | 1 | 1 | 1 | 0 | 1 | 17 | 1 | 1 | 1 | 11 | 31 | 2 | 0 | 0 | 57 | 0 | 3 | 0 | 1 | 7 | 60 | 12 | 1 | 0 | 2 | 59 | 0 | 0 | 0 | 1656 | 1 | 1 | 8 | 4 | 17 | 62 | 2 | 41 | 41 | 0 | 3 | 73 | 144 | 79 | 266 | 3 | 146 | 43 | 0 | 0 | 0 | 0 | 32 | 0 | 29 | 2 | 44 | 0 | 0 | 6 | 1 | 22 | 0 | 9 | 0 | 11947 | 5 | 2 | 1 | 373 | 666 | 2 | 5 | 1 | 20 | 0 | 3 | 6 | 18 | 501 | 8407 | 0 | 32 | 10 | 4320 | 5 | 2 | 7 | 0 | 1 | 0 | 1 | 1 |
| 2020-04-05 | 7 | 20 | 152 | 18 | 2 | 0 | 44 | 7 | 35 | 204 | 7 | 4 | 4 | 9 | 1 | 8 | 1447 | 0 | 0 | 0 | 10 | 23 | 1 | 486 | 1 | 20 | 17 | 1 | 0 | 1 | 0 | 9 | 259 | 0 | 0 | 34 | 3333 | 35 | 5 | 18 | 2 | 3 | 15 | 8 | 9 | 67 | 179 | 11 | 0 | 0 | 82 | 180 | 78 | 3 | 0 | 0 | 15 | 0 | 2 | 0 | 28 | 8093 | 1 | 1 | 2 | 1584 | 5 | 73 | 0 | 2 | 0 | 0 | 4 | 1 | 0 | 22 | 34 | 4 | 99 | 198 | 3603 | 61 | 158 | 49 | 15887 | 3 | 77 | 5 | 6 | 4 | 183 | 1 | 1 | 1 | 0 | 1 | 18 | 3 | 1 | 1 | 13 | 36 | 2 | 0 | 0 | 61 | 0 | 5 | 0 | 1 | 7 | 79 | 15 | 1 | 0 | 2 | 70 | 0 | 0 | 0 | 1771 | 1 | 1 | 10 | 5 | 18 | 71 | 2 | 47 | 46 | 0 | 3 | 83 | 152 | 94 | 295 | 4 | 151 | 45 | 0 | 0 | 0 | 0 | 32 | 0 | 34 | 2 | 51 | 0 | 0 | 6 | 1 | 28 | 0 | 11 | 0 | 12641 | 5 | 2 | 1 | 401 | 715 | 2 | 5 | 1 | 23 | 0 | 3 | 7 | 22 | 574 | 9619 | 0 | 37 | 10 | 4943 | 5 | 2 | 7 | 0 | 1 | 0 | 1 | 1 |
| 2020-04-06 | 11 | 21 | 173 | 21 | 2 | 0 | 48 | 8 | 40 | 220 | 7 | 5 | 4 | 12 | 2 | 13 | 1632 | 1 | 1 | 0 | 11 | 29 | 1 | 564 | 1 | 22 | 18 | 1 | 0 | 1 | 0 | 9 | 339 | 0 | 0 | 37 | 3335 | 46 | 5 | 18 | 2 | 3 | 16 | 9 | 9 | 78 | 187 | 11 | 0 | 0 | 86 | 191 | 85 | 4 | 0 | 0 | 19 | 0 | 2 | 0 | 27 | 8926 | 1 | 1 | 2 | 1810 | 5 | 79 | 0 | 3 | 0 | 0 | 4 | 1 | 0 | 22 | 38 | 6 | 136 | 209 | 3739 | 64 | 174 | 57 | 16523 | 3 | 85 | 6 | 6 | 6 | 186 | 1 | 1 | 4 | 0 | 1 | 19 | 3 | 1 | 1 | 15 | 41 | 2 | 0 | 0 | 62 | 0 | 5 | 0 | 1 | 7 | 94 | 19 | 1 | 0 | 2 | 80 | 0 | 0 | 0 | 1874 | 1 | 1 | 10 | 5 | 23 | 76 | 2 | 53 | 54 | 0 | 5 | 92 | 163 | 107 | 311 | 4 | 176 | 47 | 0 | 0 | 0 | 0 | 32 | 0 | 38 | 2 | 58 | 0 | 0 | 6 | 2 | 30 | 0 | 12 | 0 | 13341 | 5 | 2 | 1 | 477 | 765 | 2 | 5 | 1 | 26 | 0 | 3 | 8 | 22 | 649 | 10783 | 0 | 38 | 11 | 5385 | 6 | 2 | 7 | 0 | 1 | 0 | 1 | 1 |
| 2020-04-07 | 14 | 22 | 193 | 22 | 2 | 1 | 56 | 8 | 45 | 243 | 8 | 6 | 5 | 17 | 3 | 13 | 2035 | 1 | 1 | 0 | 14 | 33 | 1 | 686 | 1 | 23 | 19 | 1 | 0 | 1 | 0 | 9 | 375 | 0 | 0 | 43 | 3335 | 50 | 5 | 18 | 2 | 3 | 18 | 11 | 9 | 88 | 203 | 11 | 0 | 0 | 98 | 191 | 94 | 4 | 0 | 0 | 21 | 0 | 2 | 0 | 34 | 10343 | 1 | 1 | 3 | 2016 | 5 | 81 | 0 | 3 | 0 | 0 | 5 | 1 | 0 | 22 | 47 | 6 | 150 | 221 | 3872 | 65 | 210 | 65 | 17127 | 3 | 92 | 6 | 6 | 6 | 192 | 4 | 1 | 4 | 0 | 2 | 19 | 3 | 1 | 1 | 15 | 44 | 2 | 0 | 1 | 63 | 0 | 5 | 0 | 1 | 7 | 125 | 22 | 1 | 0 | 2 | 90 | 0 | 0 | 0 | 2108 | 1 | 1 | 11 | 6 | 26 | 89 | 2 | 57 | 55 | 0 | 5 | 107 | 177 | 129 | 345 | 6 | 197 | 58 | 0 | 0 | 0 | 0 | 34 | 0 | 41 | 2 | 61 | 0 | 0 | 6 | 2 | 36 | 0 | 13 | 0 | 14045 | 6 | 2 | 1 | 591 | 821 | 2 | 5 | 1 | 27 | 0 | 3 | 8 | 23 | 725 | 12722 | 0 | 45 | 12 | 6171 | 7 | 2 | 7 | 0 | 1 | 0 | 1 | 2 |
deaths.index
DatetimeIndex(['2020-01-22', '2020-01-23', '2020-01-24', '2020-01-25',
'2020-01-26', '2020-01-27', '2020-01-28', '2020-01-29',
'2020-01-30', '2020-01-31', '2020-02-01', '2020-02-02',
'2020-02-03', '2020-02-04', '2020-02-05', '2020-02-06',
'2020-02-07', '2020-02-08', '2020-02-09', '2020-02-10',
'2020-02-11', '2020-02-12', '2020-02-13', '2020-02-14',
'2020-02-15', '2020-02-16', '2020-02-17', '2020-02-18',
'2020-02-19', '2020-02-20', '2020-02-21', '2020-02-22',
'2020-02-23', '2020-02-24', '2020-02-25', '2020-02-26',
'2020-02-27', '2020-02-28', '2020-02-29', '2020-03-01',
'2020-03-02', '2020-03-03', '2020-03-04', '2020-03-05',
'2020-03-06', '2020-03-07', '2020-03-08', '2020-03-09',
'2020-03-10', '2020-03-11', '2020-03-12', '2020-03-13',
'2020-03-14', '2020-03-15', '2020-03-16', '2020-03-17',
'2020-03-18', '2020-03-19', '2020-03-20', '2020-03-21',
'2020-03-22', '2020-03-23', '2020-03-24', '2020-03-25',
'2020-03-26', '2020-03-27', '2020-03-28', '2020-03-29',
'2020-03-30', '2020-03-31', '2020-04-01', '2020-04-02',
'2020-04-03', '2020-04-04', '2020-04-05', '2020-04-06',
'2020-04-07'],
dtype='datetime64[ns]', name='Date', freq=None)
pd.set_option('display.max_columns', None)
deaths.describe()
| Country/Region | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | Austria | Azerbaijan | Bahamas | Bahrain | Bangladesh | Barbados | Belarus | Belgium | Belize | Benin | Bhutan | Bolivia | Bosnia and Herzegovina | Botswana | Brazil | Brunei | Bulgaria | Burkina Faso | Burma | Burundi | Cabo Verde | Cambodia | Cameroon | Canada | Central African Republic | Chad | Chile | China | Colombia | Congo (Brazzaville) | Congo (Kinshasa) | Costa Rica | Cote d'Ivoire | Croatia | Cuba | Cyprus | Czechia | Denmark | Diamond Princess | Djibouti | Dominica | Dominican Republic | Ecuador | Egypt | El Salvador | Equatorial Guinea | Eritrea | Estonia | Eswatini | Ethiopia | Fiji | Finland | France | Gabon | Gambia | Georgia | Germany | Ghana | Greece | Grenada | Guatemala | Guinea | Guinea-Bissau | Guyana | Haiti | Holy See | Honduras | Hungary | Iceland | India | Indonesia | Iran | Iraq | Ireland | Israel | Italy | Jamaica | Japan | Jordan | Kazakhstan | Kenya | Korea, South | Kosovo | Kuwait | Kyrgyzstan | Laos | Latvia | Lebanon | Liberia | Libya | Liechtenstein | Lithuania | Luxembourg | MS Zaandam | Madagascar | Malawi | Malaysia | Maldives | Mali | Malta | Mauritania | Mauritius | Mexico | Moldova | Monaco | Mongolia | Montenegro | Morocco | Mozambique | Namibia | Nepal | Netherlands | New Zealand | Nicaragua | Niger | Nigeria | North Macedonia | Norway | Oman | Pakistan | Panama | Papua New Guinea | Paraguay | Peru | Philippines | Poland | Portugal | Qatar | Romania | Russia | Rwanda | Saint Kitts and Nevis | Saint Lucia | Saint Vincent and the Grenadines | San Marino | Sao Tome and Principe | Saudi Arabia | Senegal | Serbia | Seychelles | Sierra Leone | Singapore | Slovakia | Slovenia | Somalia | South Africa | South Sudan | Spain | Sri Lanka | Sudan | Suriname | Sweden | Switzerland | Syria | Taiwan* | Tanzania | Thailand | Timor-Leste | Togo | Trinidad and Tobago | Tunisia | Turkey | US | Uganda | Ukraine | United Arab Emirates | United Kingdom | Uruguay | Uzbekistan | Venezuela | Vietnam | West Bank and Gaza | Western Sahara | Zambia | Zimbabwe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.00000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.0 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.0 | 77.000000 | 77.0 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.0 | 77.0 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.0 | 77.0 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.0 | 77.0 | 77.0 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.000000 | 77.0 | 77.000000 | 77.0 | 77.000000 | 77.000000 |
| mean | 1.090909 | 2.883117 | 15.857143 | 2.103896 | 0.25974 | 0.012987 | 5.870130 | 0.779221 | 5.350649 | 25.337662 | 1.025974 | 0.285714 | 0.909091 | 1.467532 | 0.077922 | 0.649351 | 162.649351 | 0.025974 | 0.025974 | 0.0 | 1.038961 | 2.623377 | 0.103896 | 52.987013 | 0.142857 | 2.428571 | 2.649351 | 0.103896 | 0.0 | 0.194805 | 0.0 | 1.051948 | 28.337662 | 0.0 | 0.0 | 3.207792 | 2195.987013 | 3.792208 | 0.272727 | 1.922078 | 0.493506 | 0.207792 | 1.532468 | 1.038961 | 1.415584 | 6.987013 | 21.272727 | 4.493506 | 0.0 | 0.0 | 9.597403 | 19.675325 | 10.935065 | 0.259740 | 0.0 | 0.0 | 1.428571 | 0.0 | 0.077922 | 0.0 | 3.103896 | 938.259740 | 0.246753 | 0.207792 | 0.103896 | 182.233766 | 0.948052 | 10.467532 | 0.0 | 0.376623 | 0.0 | 0.0 | 0.623377 | 0.038961 | 0.0 | 1.818182 | 4.376623 | 0.740260 | 11.675325 | 30.077922 | 760.090909 | 11.883117 | 16.597403 | 5.337662 | 2898.428571 | 0.441558 | 17.766234 | 0.675325 | 0.584416 | 0.441558 | 51.103896 | 0.207792 | 0.051948 | 0.142857 | 0.0 | 0.077922 | 2.987013 | 0.129870 | 0.077922 | 0.051948 | 1.649351 | 5.246753 | 0.181818 | 0.0 | 0.012987 | 8.506494 | 0.0 | 0.415584 | 0.0 | 0.116883 | 0.961039 | 7.558442 | 1.402597 | 0.129870 | 0.0 | 0.311688 | 8.077922 | 0.0 | 0.0 | 0.0 | 224.051948 | 0.129870 | 0.155844 | 0.844156 | 0.506494 | 2.051948 | 8.896104 | 0.155844 | 5.532468 | 5.597403 | 0.0 | 0.662338 | 8.454545 | 21.714286 | 10.298701 | 34.428571 | 0.389610 | 17.194805 | 4.350649 | 0.0 | 0.0 | 0.0 | 0.0 | 6.935065 | 0.0 | 3.155844 | 0.142857 | 4.974026 | 0.0 | 0.0 | 0.792208 | 0.168831 | 3.259740 | 0.0 | 0.987013 | 0.0 | 1785.532468 | 0.493506 | 0.454545 | 0.064935 | 47.948052 | 92.831169 | 0.246753 | 1.272727 | 0.103896 | 2.779221 | 0.0 | 0.311688 | 0.792208 | 2.506494 | 58.220779 | 1006.480519 | 0.0 | 4.025974 | 1.298701 | 504.350649 | 0.467532 | 0.298701 | 0.701299 | 0.0 | 0.168831 | 0.0 | 0.077922 | 0.220779 |
| std | 2.601527 | 5.842165 | 39.367939 | 5.317757 | 0.67673 | 0.113961 | 12.855831 | 2.062340 | 9.753227 | 58.164832 | 1.946481 | 1.086486 | 1.590943 | 3.101716 | 0.421970 | 2.382868 | 410.243534 | 0.160101 | 0.160101 | 0.0 | 2.993157 | 6.747395 | 0.307127 | 136.538263 | 0.352222 | 5.115581 | 5.457410 | 0.307127 | 0.0 | 0.398648 | 0.0 | 2.579760 | 72.880510 | 0.0 | 0.0 | 8.680495 | 1230.823820 | 10.051627 | 1.034107 | 4.672896 | 0.852602 | 0.635313 | 3.761244 | 2.330809 | 3.096478 | 18.631110 | 48.073583 | 4.099497 | 0.0 | 0.0 | 22.960641 | 47.113089 | 22.431966 | 0.849188 | 0.0 | 0.0 | 4.240868 | 0.0 | 0.389542 | 0.0 | 7.564781 | 2222.204604 | 0.433949 | 0.408388 | 0.475295 | 442.151674 | 1.863049 | 20.997233 | 0.0 | 0.669622 | 0.0 | 0.0 | 1.158935 | 0.194771 | 0.0 | 5.197994 | 9.694055 | 1.454599 | 29.528952 | 59.599048 | 1172.063017 | 19.207985 | 43.007423 | 13.793220 | 4995.124788 | 0.895802 | 24.560289 | 1.712505 | 1.533406 | 1.261719 | 62.276671 | 0.569803 | 0.223377 | 0.663212 | 0.0 | 0.314820 | 5.285266 | 0.592733 | 0.269807 | 0.223377 | 3.684128 | 10.821708 | 0.578730 | 0.0 | 0.113961 | 17.737578 | 0.0 | 1.184890 | 0.0 | 0.323388 | 2.086558 | 21.871932 | 4.020539 | 0.338365 | 0.0 | 0.654131 | 19.222907 | 0.0 | 0.0 | 0.0 | 500.221816 | 0.338365 | 0.365086 | 2.373455 | 1.263072 | 5.253811 | 19.657986 | 0.488417 | 13.044039 | 13.018468 | 0.0 | 1.313997 | 22.150529 | 42.810125 | 25.412940 | 81.435869 | 1.113976 | 43.882930 | 12.091500 | 0.0 | 0.0 | 0.0 | 0.0 | 10.802458 | 0.0 | 8.843768 | 0.478877 | 13.478027 | 0.0 | 0.0 | 1.641066 | 0.441367 | 7.510348 | 0.0 | 2.835366 | 0.0 | 3709.589382 | 1.372890 | 0.698597 | 0.248027 | 119.254471 | 200.849669 | 0.652038 | 1.509935 | 0.307127 | 6.103544 | 0.0 | 0.815310 | 1.962394 | 5.651323 | 152.829336 | 2594.091807 | 0.0 | 9.598484 | 2.924780 | 1293.356433 | 1.438058 | 0.708314 | 1.878304 | 0.0 | 0.377059 | 0.0 | 0.269807 | 0.447901 |
| min | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 17.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 |
| 25% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 1012.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 |
| 50% | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.00000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 2837.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 6.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 2.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 43.000000 | 0.000000 | 0.000000 | 0.000000 | 29.000000 | 0.000000 | 5.000000 | 0.000000 | 0.000000 | 0.000000 | 16.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 |
| 75% | 0.000000 | 2.000000 | 9.000000 | 0.000000 | 0.00000 | 0.000000 | 3.000000 | 0.000000 | 6.000000 | 6.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 21.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 6.000000 | 0.000000 | 3.000000 | 1.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 9.000000 | 0.0 | 0.0 | 0.000000 | 3249.000000 | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.000000 | 6.000000 | 7.000000 | 0.0 | 0.0 | 2.000000 | 3.000000 | 6.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 244.000000 | 0.000000 | 0.000000 | 0.000000 | 44.000000 | 0.000000 | 6.000000 | 0.0 | 1.000000 | 0.0 | 0.0 | 1.000000 | 0.000000 | 0.0 | 0.000000 | 1.000000 | 1.000000 | 4.000000 | 25.000000 | 1284.000000 | 13.000000 | 3.000000 | 0.000000 | 3405.000000 | 1.000000 | 29.000000 | 0.000000 | 0.000000 | 0.000000 | 91.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 4.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 4.000000 | 0.000000 | 0.0 | 0.000000 | 2.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | 0.000000 | 0.0 | 0.000000 | 2.000000 | 0.0 | 0.0 | 0.0 | 77.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 7.000000 | 0.000000 | 2.000000 | 1.000000 | 0.0 | 0.000000 | 0.000000 | 18.000000 | 5.000000 | 3.000000 | 0.000000 | 0.000000 | 1.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 11.000000 | 0.0 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.0 | 0.000000 | 0.000000 | 1.000000 | 0.0 | 0.000000 | 0.0 | 830.000000 | 0.000000 | 1.000000 | 0.000000 | 11.000000 | 41.000000 | 0.000000 | 1.000000 | 0.000000 | 1.000000 | 0.0 | 0.000000 | 0.000000 | 1.000000 | 3.000000 | 200.000000 | 0.0 | 2.000000 | 0.000000 | 138.000000 | 0.000000 | 0.000000 | 0.000000 | 0.0 | 0.000000 | 0.0 | 0.000000 | 0.000000 |
| max | 14.000000 | 22.000000 | 193.000000 | 22.000000 | 2.00000 | 1.000000 | 56.000000 | 8.000000 | 45.000000 | 243.000000 | 8.000000 | 6.000000 | 5.000000 | 17.000000 | 3.000000 | 13.000000 | 2035.000000 | 1.000000 | 1.000000 | 0.0 | 14.000000 | 33.000000 | 1.000000 | 686.000000 | 1.000000 | 23.000000 | 19.000000 | 1.000000 | 0.0 | 1.000000 | 0.0 | 9.000000 | 375.000000 | 0.0 | 0.0 | 43.000000 | 3335.000000 | 50.000000 | 5.000000 | 18.000000 | 2.000000 | 3.000000 | 18.000000 | 11.000000 | 11.000000 | 88.000000 | 203.000000 | 11.000000 | 0.0 | 0.0 | 98.000000 | 191.000000 | 94.000000 | 4.000000 | 0.0 | 0.0 | 21.000000 | 0.0 | 2.000000 | 0.0 | 34.000000 | 10343.000000 | 1.000000 | 1.000000 | 3.000000 | 2016.000000 | 5.000000 | 81.000000 | 0.0 | 3.000000 | 0.0 | 0.0 | 5.000000 | 1.000000 | 0.0 | 22.000000 | 47.000000 | 6.000000 | 150.000000 | 221.000000 | 3872.000000 | 65.000000 | 210.000000 | 65.000000 | 17127.000000 | 3.000000 | 92.000000 | 6.000000 | 6.000000 | 6.000000 | 192.000000 | 4.000000 | 1.000000 | 4.000000 | 0.0 | 2.000000 | 19.000000 | 3.000000 | 1.000000 | 1.000000 | 15.000000 | 44.000000 | 2.000000 | 0.0 | 1.000000 | 63.000000 | 0.0 | 5.000000 | 0.0 | 1.000000 | 7.000000 | 125.000000 | 22.000000 | 1.000000 | 0.0 | 2.000000 | 90.000000 | 0.0 | 0.0 | 0.0 | 2108.000000 | 1.000000 | 1.000000 | 11.000000 | 6.000000 | 26.000000 | 89.000000 | 2.000000 | 57.000000 | 55.000000 | 0.0 | 5.000000 | 107.000000 | 177.000000 | 129.000000 | 345.000000 | 6.000000 | 197.000000 | 58.000000 | 0.0 | 0.0 | 0.0 | 0.0 | 34.000000 | 0.0 | 41.000000 | 2.000000 | 61.000000 | 0.0 | 0.0 | 6.000000 | 2.000000 | 36.000000 | 0.0 | 13.000000 | 0.0 | 14045.000000 | 6.000000 | 2.000000 | 1.000000 | 591.000000 | 821.000000 | 2.000000 | 5.000000 | 1.000000 | 27.000000 | 0.0 | 3.000000 | 8.000000 | 23.000000 | 725.000000 | 12722.000000 | 0.0 | 45.000000 | 12.000000 | 6171.000000 | 7.000000 | 2.000000 | 7.000000 | 0.0 | 1.000000 | 0.0 | 1.000000 | 2.000000 |
print("Kasus terkecil namun paling tinggi:", deaths.min().max())
print("Nilai tertinggi dan paling besar nilainya diantara yang lain: ",deaths.max().max())
Kasus terkecil namun paling tinggi: 17 Nilai tertinggi dan paling besar nilainya diantara yang lain: 17127
Berdasarkan tabel-tabel diatas didapatkan juga informasi sebagai berikut:
1. Bhutan 21.Nepal
2. Burundi 22.Nimbia
3. Cambodia 23.Mozambique
4. Chad 24.Papua New Guniea
5. Central African Republic 25.Rwanda
6. Dominica 26.Saint Kitts and Nevis
7. Djibouti 27.Saint Lucia
8. Equatriorial Guniea 28.Saint Vincent and the Grenadines
9. Eritrea 29.Sao Tome and Principe
10.Eswatini 30.Sierra Leone
11.Fiji 31.Seychelles
12.Grenada 32.Somalia
13.Guinea 33.South Sudan
14.Guinea-Bissau 34.Timor Leste
15.Holy See 35.Uganda
16.Laos 36.Vietnam
17.Madagascar 37.Western Sahara
18.Maldives
19.Malta
20.Mongolia
Untuk mendapatkan gambaran perkembangan kasus covid-19 di berbagai negara, diperlukan penyelarasan kurva pertumbuhan setiap negara. Kurva hanya menampilkan informasi yang dimulai dengan hari dimana data kasus kematian COVID-19 setidaknya 25 orang.
deaths = raw_data_deaths.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
value_vars=raw_data_deaths.iloc[:,4:],
var_name='Date', value_name='Total Pasien Meninggal')
deaths["Date"] = pd.to_datetime(deaths["Date"])
deaths = deaths.drop(["Province/State","Lat","Long"], axis = 1)
deaths = deaths.groupby(['Date','Country/Region'],as_index=False).agg({'Total Pasien Meninggal': 'sum'})
deaths = deaths.pivot_table(index=['Date'], columns='Country/Region',
values='Total Pasien Meninggal', aggfunc='first').reset_index()
deaths = deaths.set_index(['Date'])
deaths
| Country/Region | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | Austria | Azerbaijan | Bahamas | Bahrain | Bangladesh | Barbados | Belarus | Belgium | Belize | Benin | Bhutan | Bolivia | Bosnia and Herzegovina | Botswana | Brazil | Brunei | Bulgaria | Burkina Faso | Burma | Burundi | Cabo Verde | Cambodia | Cameroon | Canada | Central African Republic | Chad | Chile | China | Colombia | Congo (Brazzaville) | Congo (Kinshasa) | Costa Rica | Cote d'Ivoire | Croatia | Cuba | Cyprus | Czechia | Denmark | Diamond Princess | Djibouti | Dominica | Dominican Republic | Ecuador | Egypt | El Salvador | Equatorial Guinea | Eritrea | Estonia | Eswatini | Ethiopia | Fiji | Finland | France | Gabon | Gambia | Georgia | Germany | Ghana | Greece | Grenada | Guatemala | Guinea | Guinea-Bissau | Guyana | Haiti | Holy See | Honduras | Hungary | Iceland | India | Indonesia | Iran | Iraq | Ireland | Israel | Italy | Jamaica | Japan | Jordan | Kazakhstan | Kenya | Korea, South | Kosovo | Kuwait | Kyrgyzstan | Laos | Latvia | Lebanon | Liberia | Libya | Liechtenstein | Lithuania | Luxembourg | MS Zaandam | Madagascar | Malawi | Malaysia | Maldives | Mali | Malta | Mauritania | Mauritius | Mexico | Moldova | Monaco | Mongolia | Montenegro | Morocco | Mozambique | Namibia | Nepal | Netherlands | New Zealand | Nicaragua | Niger | Nigeria | North Macedonia | Norway | Oman | Pakistan | Panama | Papua New Guinea | Paraguay | Peru | Philippines | Poland | Portugal | Qatar | Romania | Russia | Rwanda | Saint Kitts and Nevis | Saint Lucia | Saint Vincent and the Grenadines | San Marino | Sao Tome and Principe | Saudi Arabia | Senegal | Serbia | Seychelles | Sierra Leone | Singapore | Slovakia | Slovenia | Somalia | South Africa | South Sudan | Spain | Sri Lanka | Sudan | Suriname | Sweden | Switzerland | Syria | Taiwan* | Tanzania | Thailand | Timor-Leste | Togo | Trinidad and Tobago | Tunisia | Turkey | US | Uganda | Ukraine | United Arab Emirates | United Kingdom | Uruguay | Uzbekistan | Venezuela | Vietnam | West Bank and Gaza | Western Sahara | Zambia | Zimbabwe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 2020-01-22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 42 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 56 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2020-04-03 | 6 | 17 | 105 | 16 | 2 | 0 | 39 | 7 | 28 | 168 | 5 | 1 | 4 | 6 | 0 | 4 | 1143 | 0 | 0 | 0 | 9 | 17 | 1 | 359 | 1 | 14 | 16 | 1 | 0 | 1 | 0 | 8 | 179 | 0 | 0 | 22 | 3326 | 25 | 2 | 13 | 2 | 1 | 8 | 6 | 11 | 53 | 139 | 11 | 0 | 0 | 68 | 145 | 66 | 2 | 0 | 0 | 12 | 0 | 0 | 0 | 20 | 6520 | 1 | 1 | 0 | 1275 | 5 | 63 | 0 | 1 | 0 | 0 | 4 | 0 | 0 | 15 | 26 | 4 | 72 | 181 | 3294 | 54 | 120 | 40 | 14681 | 3 | 63 | 5 | 6 | 4 | 174 | 1 | 0 | 1 | 0 | 1 | 17 | 0 | 1 | 0 | 9 | 31 | 2 | 0 | 0 | 53 | 0 | 3 | 0 | 1 | 7 | 50 | 8 | 1 | 0 | 2 | 48 | 0 | 0 | 0 | 1490 | 1 | 1 | 5 | 4 | 12 | 59 | 1 | 40 | 37 | 0 | 3 | 61 | 136 | 71 | 246 | 3 | 133 | 34 | 0 | 0 | 0 | 0 | 30 | 0 | 25 | 1 | 39 | 0 | 0 | 5 | 1 | 20 | 0 | 9 | 0 | 11198 | 4 | 2 | 1 | 358 | 591 | 2 | 5 | 1 | 19 | 0 | 3 | 6 | 18 | 425 | 7087 | 0 | 27 | 9 | 3611 | 4 | 2 | 7 | 0 | 1 | 0 | 1 | 1 |
| 2020-04-04 | 7 | 20 | 130 | 17 | 2 | 0 | 43 | 7 | 30 | 186 | 5 | 4 | 4 | 8 | 0 | 5 | 1283 | 0 | 0 | 0 | 10 | 21 | 1 | 445 | 1 | 17 | 16 | 1 | 0 | 1 | 0 | 9 | 218 | 0 | 0 | 27 | 3330 | 32 | 2 | 18 | 2 | 1 | 12 | 6 | 11 | 59 | 161 | 11 | 0 | 0 | 68 | 172 | 71 | 3 | 0 | 0 | 13 | 0 | 0 | 0 | 25 | 7574 | 1 | 1 | 1 | 1444 | 5 | 68 | 0 | 2 | 0 | 0 | 4 | 0 | 0 | 15 | 32 | 4 | 86 | 191 | 3452 | 56 | 137 | 44 | 15362 | 3 | 77 | 5 | 5 | 4 | 177 | 1 | 1 | 1 | 0 | 1 | 17 | 1 | 1 | 1 | 11 | 31 | 2 | 0 | 0 | 57 | 0 | 3 | 0 | 1 | 7 | 60 | 12 | 1 | 0 | 2 | 59 | 0 | 0 | 0 | 1656 | 1 | 1 | 8 | 4 | 17 | 62 | 2 | 41 | 41 | 0 | 3 | 73 | 144 | 79 | 266 | 3 | 146 | 43 | 0 | 0 | 0 | 0 | 32 | 0 | 29 | 2 | 44 | 0 | 0 | 6 | 1 | 22 | 0 | 9 | 0 | 11947 | 5 | 2 | 1 | 373 | 666 | 2 | 5 | 1 | 20 | 0 | 3 | 6 | 18 | 501 | 8407 | 0 | 32 | 10 | 4320 | 5 | 2 | 7 | 0 | 1 | 0 | 1 | 1 |
| 2020-04-05 | 7 | 20 | 152 | 18 | 2 | 0 | 44 | 7 | 35 | 204 | 7 | 4 | 4 | 9 | 1 | 8 | 1447 | 0 | 0 | 0 | 10 | 23 | 1 | 486 | 1 | 20 | 17 | 1 | 0 | 1 | 0 | 9 | 259 | 0 | 0 | 34 | 3333 | 35 | 5 | 18 | 2 | 3 | 15 | 8 | 9 | 67 | 179 | 11 | 0 | 0 | 82 | 180 | 78 | 3 | 0 | 0 | 15 | 0 | 2 | 0 | 28 | 8093 | 1 | 1 | 2 | 1584 | 5 | 73 | 0 | 2 | 0 | 0 | 4 | 1 | 0 | 22 | 34 | 4 | 99 | 198 | 3603 | 61 | 158 | 49 | 15887 | 3 | 77 | 5 | 6 | 4 | 183 | 1 | 1 | 1 | 0 | 1 | 18 | 3 | 1 | 1 | 13 | 36 | 2 | 0 | 0 | 61 | 0 | 5 | 0 | 1 | 7 | 79 | 15 | 1 | 0 | 2 | 70 | 0 | 0 | 0 | 1771 | 1 | 1 | 10 | 5 | 18 | 71 | 2 | 47 | 46 | 0 | 3 | 83 | 152 | 94 | 295 | 4 | 151 | 45 | 0 | 0 | 0 | 0 | 32 | 0 | 34 | 2 | 51 | 0 | 0 | 6 | 1 | 28 | 0 | 11 | 0 | 12641 | 5 | 2 | 1 | 401 | 715 | 2 | 5 | 1 | 23 | 0 | 3 | 7 | 22 | 574 | 9619 | 0 | 37 | 10 | 4943 | 5 | 2 | 7 | 0 | 1 | 0 | 1 | 1 |
| 2020-04-06 | 11 | 21 | 173 | 21 | 2 | 0 | 48 | 8 | 40 | 220 | 7 | 5 | 4 | 12 | 2 | 13 | 1632 | 1 | 1 | 0 | 11 | 29 | 1 | 564 | 1 | 22 | 18 | 1 | 0 | 1 | 0 | 9 | 339 | 0 | 0 | 37 | 3335 | 46 | 5 | 18 | 2 | 3 | 16 | 9 | 9 | 78 | 187 | 11 | 0 | 0 | 86 | 191 | 85 | 4 | 0 | 0 | 19 | 0 | 2 | 0 | 27 | 8926 | 1 | 1 | 2 | 1810 | 5 | 79 | 0 | 3 | 0 | 0 | 4 | 1 | 0 | 22 | 38 | 6 | 136 | 209 | 3739 | 64 | 174 | 57 | 16523 | 3 | 85 | 6 | 6 | 6 | 186 | 1 | 1 | 4 | 0 | 1 | 19 | 3 | 1 | 1 | 15 | 41 | 2 | 0 | 0 | 62 | 0 | 5 | 0 | 1 | 7 | 94 | 19 | 1 | 0 | 2 | 80 | 0 | 0 | 0 | 1874 | 1 | 1 | 10 | 5 | 23 | 76 | 2 | 53 | 54 | 0 | 5 | 92 | 163 | 107 | 311 | 4 | 176 | 47 | 0 | 0 | 0 | 0 | 32 | 0 | 38 | 2 | 58 | 0 | 0 | 6 | 2 | 30 | 0 | 12 | 0 | 13341 | 5 | 2 | 1 | 477 | 765 | 2 | 5 | 1 | 26 | 0 | 3 | 8 | 22 | 649 | 10783 | 0 | 38 | 11 | 5385 | 6 | 2 | 7 | 0 | 1 | 0 | 1 | 1 |
| 2020-04-07 | 14 | 22 | 193 | 22 | 2 | 1 | 56 | 8 | 45 | 243 | 8 | 6 | 5 | 17 | 3 | 13 | 2035 | 1 | 1 | 0 | 14 | 33 | 1 | 686 | 1 | 23 | 19 | 1 | 0 | 1 | 0 | 9 | 375 | 0 | 0 | 43 | 3335 | 50 | 5 | 18 | 2 | 3 | 18 | 11 | 9 | 88 | 203 | 11 | 0 | 0 | 98 | 191 | 94 | 4 | 0 | 0 | 21 | 0 | 2 | 0 | 34 | 10343 | 1 | 1 | 3 | 2016 | 5 | 81 | 0 | 3 | 0 | 0 | 5 | 1 | 0 | 22 | 47 | 6 | 150 | 221 | 3872 | 65 | 210 | 65 | 17127 | 3 | 92 | 6 | 6 | 6 | 192 | 4 | 1 | 4 | 0 | 2 | 19 | 3 | 1 | 1 | 15 | 44 | 2 | 0 | 1 | 63 | 0 | 5 | 0 | 1 | 7 | 125 | 22 | 1 | 0 | 2 | 90 | 0 | 0 | 0 | 2108 | 1 | 1 | 11 | 6 | 26 | 89 | 2 | 57 | 55 | 0 | 5 | 107 | 177 | 129 | 345 | 6 | 197 | 58 | 0 | 0 | 0 | 0 | 34 | 0 | 41 | 2 | 61 | 0 | 0 | 6 | 2 | 36 | 0 | 13 | 0 | 14045 | 6 | 2 | 1 | 591 | 821 | 2 | 5 | 1 | 27 | 0 | 3 | 8 | 23 | 725 | 12722 | 0 | 45 | 12 | 6171 | 7 | 2 | 7 | 0 | 1 | 0 | 1 | 2 |
77 rows × 184 columns
pd.set_option('display.max_columns', None)
kurangdaridualima = deaths[-1:].squeeze() >= 25
x = deaths.loc[:, kurangdaridualima]
death = pd.DataFrame(x)
death
| Country/Region | Algeria | Argentina | Australia | Austria | Belgium | Bosnia and Herzegovina | Brazil | Canada | Chile | China | Colombia | Czechia | Denmark | Dominican Republic | Ecuador | Egypt | Finland | France | Germany | Greece | Hungary | India | Indonesia | Iran | Iraq | Ireland | Israel | Italy | Japan | Korea, South | Luxembourg | Malaysia | Mexico | Morocco | Netherlands | North Macedonia | Norway | Pakistan | Panama | Peru | Philippines | Poland | Portugal | Romania | Russia | San Marino | Saudi Arabia | Serbia | Slovenia | Spain | Sweden | Switzerland | Thailand | Turkey | US | Ukraine | United Kingdom |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 2020-01-22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 17 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 18 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 42 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 56 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2020-04-03 | 105 | 39 | 28 | 168 | 1143 | 17 | 359 | 179 | 22 | 3326 | 25 | 53 | 139 | 68 | 145 | 66 | 20 | 6520 | 1275 | 63 | 26 | 72 | 181 | 3294 | 54 | 120 | 40 | 14681 | 63 | 174 | 31 | 53 | 50 | 48 | 1490 | 12 | 59 | 40 | 37 | 61 | 136 | 71 | 246 | 133 | 34 | 30 | 25 | 39 | 20 | 11198 | 358 | 591 | 19 | 425 | 7087 | 27 | 3611 |
| 2020-04-04 | 130 | 43 | 30 | 186 | 1283 | 21 | 445 | 218 | 27 | 3330 | 32 | 59 | 161 | 68 | 172 | 71 | 25 | 7574 | 1444 | 68 | 32 | 86 | 191 | 3452 | 56 | 137 | 44 | 15362 | 77 | 177 | 31 | 57 | 60 | 59 | 1656 | 17 | 62 | 41 | 41 | 73 | 144 | 79 | 266 | 146 | 43 | 32 | 29 | 44 | 22 | 11947 | 373 | 666 | 20 | 501 | 8407 | 32 | 4320 |
| 2020-04-05 | 152 | 44 | 35 | 204 | 1447 | 23 | 486 | 259 | 34 | 3333 | 35 | 67 | 179 | 82 | 180 | 78 | 28 | 8093 | 1584 | 73 | 34 | 99 | 198 | 3603 | 61 | 158 | 49 | 15887 | 77 | 183 | 36 | 61 | 79 | 70 | 1771 | 18 | 71 | 47 | 46 | 83 | 152 | 94 | 295 | 151 | 45 | 32 | 34 | 51 | 28 | 12641 | 401 | 715 | 23 | 574 | 9619 | 37 | 4943 |
| 2020-04-06 | 173 | 48 | 40 | 220 | 1632 | 29 | 564 | 339 | 37 | 3335 | 46 | 78 | 187 | 86 | 191 | 85 | 27 | 8926 | 1810 | 79 | 38 | 136 | 209 | 3739 | 64 | 174 | 57 | 16523 | 85 | 186 | 41 | 62 | 94 | 80 | 1874 | 23 | 76 | 53 | 54 | 92 | 163 | 107 | 311 | 176 | 47 | 32 | 38 | 58 | 30 | 13341 | 477 | 765 | 26 | 649 | 10783 | 38 | 5385 |
| 2020-04-07 | 193 | 56 | 45 | 243 | 2035 | 33 | 686 | 375 | 43 | 3335 | 50 | 88 | 203 | 98 | 191 | 94 | 34 | 10343 | 2016 | 81 | 47 | 150 | 221 | 3872 | 65 | 210 | 65 | 17127 | 92 | 192 | 44 | 63 | 125 | 90 | 2108 | 26 | 89 | 57 | 55 | 107 | 177 | 129 | 345 | 197 | 58 | 34 | 41 | 61 | 36 | 14045 | 591 | 821 | 27 | 725 | 12722 | 45 | 6171 |
77 rows × 57 columns
print("Kasus Kematian terkecil namun paling tinggi:", death.min().max())
print("Kasus Kematian maximum namun paling besar nilainya: ",death.max().max())
print("Kasus Kematian maximum namun paling kecil nilainya: ",death.max().min())
Kasus Kematian terkecil namun paling tinggi: 17 Kasus Kematian maximum namun paling besar nilainya: 17127 Kasus Kematian maximum namun paling kecil nilainya: 26
Berdasarkan tabel diatas dari 184 negara, hanya 57 negara saja yang memiliki jumlah kematian lebih dari sama dengan 25 pada periode akhir (7 April 2020). Dari ke-57 negara tersebut yang kasus kematian pertama paling tinggi bernilai 17 yakni berasal dari negara China (tanggal 22 Januari 2020). Sedangkan pada akhir periode, kasus kematian terbanyak ialah berasal dari negara Italia dengan total akhir kasus kematian mencapai 17.127. Selanjutnya negara dengan kasus terkecil pada periode akhir berasal dari negara North Macedonia dengan total kasus kematian hanya mencapai 26 di akhir periode.
#untuk mnegetahui masing-masing tanggal dimulai dan berakhir tiap negara
algeria = death['Algeria']
Algeria = pd.DataFrame(algeria)
Algeria = Algeria[Algeria['Algeria']>=25]
Algeria
| Algeria | |
|---|---|
| Date | |
| 2020-03-26 | 25 |
| 2020-03-27 | 26 |
| 2020-03-28 | 29 |
| 2020-03-29 | 31 |
| 2020-03-30 | 35 |
| 2020-03-31 | 44 |
| 2020-04-01 | 58 |
| 2020-04-02 | 86 |
| 2020-04-03 | 105 |
| 2020-04-04 | 130 |
| 2020-04-05 | 152 |
| 2020-04-06 | 173 |
| 2020-04-07 | 193 |
Visualisasikan hasil diatas dan berikan/atur judul, labels, dan spesifikasi (ukuran, warna, ketebalan, dll) yang sesuai sehingga plot/kurva yang dihasilkan rapi, menarik, dan mudah dipahami.
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
Algeria = pd.merge(confirmed_country["Algeria"], death["Algeria"], left_index=True, right_index=True)
Algeria.rename(columns = {'Algeria_x':'Confirmed Cases'}, inplace = True)
Algeria.rename(columns = {'Algeria_y':'Death Cases'}, inplace = True)
Algeria["2020-03-26":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Algeria')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.gcf().set_size_inches(8, 7)
Argentina = pd.merge(confirmed_country["Argentina"], death["Argentina"], left_index=True, right_index=True)
Argentina.rename(columns = {'Argentina_x':'Confirmed Cases'}, inplace = True)
Argentina.rename(columns = {'Argentina_y':'Death Cases'}, inplace = True)
Argentina["2020-03-31":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Argentina')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Australia = pd.merge(confirmed_country["Australia"], death["Australia"], left_index=True, right_index=True)
Australia.rename(columns = {'Australia_x':'Confirmed Cases'}, inplace = True)
Australia.rename(columns = {'Australia_y':'Death Cases'}, inplace = True)
Australia["2020-04-03":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Australia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Austria = pd.merge(confirmed_country["Austria"], death["Austria"], left_index=True, right_index=True)
Austria.rename(columns = {'Austria_x':'Confirmed Cases'}, inplace = True)
Austria.rename(columns = {'Austria_y':'Death Cases'}, inplace = True)
Austria["2020-03-24":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Austria')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Belgium = pd.merge(confirmed_country["Belgium"], death["Belgium"], left_index=True, right_index=True)
Belgium.rename(columns = {'Belgium_x':'Confirmed Cases'}, inplace = True)
Belgium.rename(columns = {'Belgium_y':'Death Cases'}, inplace = True)
Belgium["2020-03-20":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Belgium')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
bosniaandherzegovina = pd.merge(confirmed_country["Bosnia and Herzegovina"], death["Bosnia and Herzegovina"], left_index=True, right_index=True)
bosniaandherzegovina.rename(columns = {'Bosnia and Herzegovina_x':'Confirmed Cases'}, inplace = True)
bosniaandherzegovina.rename(columns = {'Bosnia and Herzegovina_y':'Death Cases'}, inplace = True)
bosniaandherzegovina["2020-04-06":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Bosnia and Herzegovina')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Brazil = pd.merge(confirmed_country["Brazil"], death["Brazil"], left_index=True, right_index=True)
Brazil.rename(columns = {'Brazil_x':'Confirmed Cases'}, inplace = True)
Brazil.rename(columns = {'Brazil_y':'Death Cases'}, inplace = True)
Brazil["2020-03-22":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Brazil')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Canada = pd.merge(confirmed_country["Canada"], death["Canada"], left_index=True, right_index=True)
Canada.rename(columns = {'Canada_x':'Confirmed Cases'}, inplace = True)
Canada.rename(columns = {'Canada_y':'Death Cases'}, inplace = True)
Canada["2020-03-23":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Canada')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Chile = pd.merge(confirmed_country["Chile"], death["Chile"], left_index=True, right_index=True)
Chile.rename(columns = {'Chile_x':'Confirmed Cases'}, inplace = True)
Chile.rename(columns = {'Chile_y':'Death Cases'}, inplace = True)
Chile["2020-04-04":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Chile')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
China = pd.merge(confirmed_country["China"], death["China"], left_index=True, right_index=True)
China.rename(columns = {'China_x':'Confirmed Cases'}, inplace = True)
China.rename(columns = {'China_y':'Death Cases'}, inplace = True)
China["2020-01-24":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di China')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Colombia = pd.merge(confirmed_country["Colombia"], death["Colombia"], left_index=True, right_index=True)
Colombia.rename(columns = {'Colombia_x':'Confirmed Cases'}, inplace = True)
Colombia.rename(columns = {'Colombia_y':'Death Cases'}, inplace = True)
Colombia["2020-04-03":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Colombia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Czechia = pd.merge(confirmed_country["Czechia"], death["Czechia"], left_index=True, right_index=True)
Czechia.rename(columns = {'Czechia_x':'Confirmed Cases'}, inplace = True)
Czechia.rename(columns = {'Czechia_y':'Death Cases'}, inplace = True)
Czechia["2020-03-31":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Czechia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Denmark = pd.merge(confirmed_country["Denmark"], death["Denmark"], left_index=True, right_index=True)
Denmark.rename(columns = {'Denmark_x':'Confirmed Cases'}, inplace = True)
Denmark.rename(columns = {'Denmark_y':'Death Cases'}, inplace = True)
Denmark["2020-03-24":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Denmark')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
dominicanrepublic = pd.merge(confirmed_country["Dominican Republic"], death["Dominican Republic"], left_index=True, right_index=True)
dominicanrepublic.rename(columns = {'Dominican Republic_x':'Confirmed Cases'}, inplace = True)
dominicanrepublic.rename(columns = {'Dominican Republic_y':'Death Cases'}, inplace = True)
dominicanrepublic["2020-03-28":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Dominican Republic')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Ecuador = pd.merge(confirmed_country["Ecuador"], death["Ecuador"], left_index=True, right_index=True)
Ecuador.rename(columns = {'Ecuador_x':'Confirmed Cases'}, inplace = True)
Ecuador.rename(columns = {'Ecuador_y':'Death Cases'}, inplace = True)
Ecuador["2020-03-24":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Ecuador')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Egypt = pd.merge(confirmed_country["Egypt"], death["Egypt"], left_index=True, right_index=True)
Egypt.rename(columns = {'Egypt_x':'Confirmed Cases'}, inplace = True)
Egypt.rename(columns = {'Egypt_y':'Death Cases'}, inplace = True)
Egypt["2020-03-27":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Egypt')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Finland = pd.merge(confirmed_country["Finland"], death["Finland"], left_index=True, right_index=True)
Finland.rename(columns = {'Finland_x':'Confirmed Cases'}, inplace = True)
Finland.rename(columns = {'Finland_y':'Death Cases'}, inplace = True)
Finland["2020-04-04":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Finland')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
France = pd.merge(confirmed_country["France"], death["France"], left_index=True, right_index=True)
France.rename(columns = {'France_x':'Confirmed Cases'}, inplace = True)
France.rename(columns = {'France_y':'Death Cases'}, inplace = True)
France["2020-03-10":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di France')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Germany = pd.merge(confirmed_country["Germany"], death["Germany"], left_index=True, right_index=True)
Germany.rename(columns = {'Germany_x':'Confirmed Cases'}, inplace = True)
Germany.rename(columns = {'Germany_y':'Death Cases'}, inplace = True)
Germany["2020-03-18":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Germany')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Greece = pd.merge(confirmed_country["Greece"], death["Greece"], left_index=True, right_index=True)
Greece.rename(columns = {'Greece_x':'Confirmed Cases'}, inplace = True)
Greece.rename(columns = {'Greece_y':'Death Cases'}, inplace = True)
Greece["2020-03-26":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Greece')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Hungary = pd.merge(confirmed_country["Hungary"], death["Hungary"], left_index=True, right_index=True)
Hungary.rename(columns = {'Hungary_x':'Confirmed Cases'}, inplace = True)
Hungary.rename(columns = {'Hungary_y':'Death Cases'}, inplace = True)
Hungary["2020-04-03":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Hungary')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
India = pd.merge(confirmed_country["India"], death["India"], left_index=True, right_index=True)
India.rename(columns = {'India_x':'Confirmed Cases'}, inplace = True)
India.rename(columns = {'India_y':'Death Cases'}, inplace = True)
India["2020-03-29":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di India')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Indonesia = pd.merge(confirmed_country["Indonesia"], death["Indonesia"], left_index=True, right_index=True)
Indonesia.rename(columns = {'Indonesia_x':'Confirmed Cases'}, inplace = True)
Indonesia.rename(columns = {'Indonesia_y':'Death Cases'}, inplace = True)
Indonesia["2020-03-19":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Indonesia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Iran = pd.merge(confirmed_country["Iran"], death["Iran"], left_index=True, right_index=True)
Iran.rename(columns = {'Iran_x':'Confirmed Cases'}, inplace = True)
Iran.rename(columns = {'Iran_y':'Death Cases'}, inplace = True)
Iran["2020-02-27":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Iran')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Iraq = pd.merge(confirmed_country["Iraq"], death["Iraq"], left_index=True, right_index=True)
Iraq.rename(columns = {'Iraq_x':'Confirmed Cases'}, inplace = True)
Iraq.rename(columns = {'Iraq_y':'Death Cases'}, inplace = True)
Iraq["2020-03-24":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Iraq')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Ireland = pd.merge(confirmed_country["Ireland"], death["Ireland"], left_index=True, right_index=True)
Ireland.rename(columns = {'Ireland_x':'Confirmed Cases'}, inplace = True)
Ireland.rename(columns = {'Ireland_y':'Death Cases'}, inplace = True)
Ireland["2020-03-28":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Ireland')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Israel = pd.merge(confirmed_country["Israel"], death["Israel"], left_index=True, right_index=True)
Israel.rename(columns = {'Israel_x':'Confirmed Cases'}, inplace = True)
Israel.rename(columns = {'Israel_y':'Death Cases'}, inplace = True)
Israel["2020-04-01":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Israel')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Italy = pd.merge(confirmed_country["Italy"], death["Italy"], left_index=True, right_index=True)
Italy.rename(columns = {'Italy_x':'Confirmed Cases'}, inplace = True)
Italy.rename(columns = {'Italy_y':'Death Cases'}, inplace = True)
Italy["2020-02-29":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Italy')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Japan = pd.merge(confirmed_country["Japan"], death["Japan"], left_index=True, right_index=True)
Japan.rename(columns = {'Japan_x':'Confirmed Cases'}, inplace = True)
Japan.rename(columns = {'Japan_y':'Death Cases'}, inplace = True)
Japan["2020-03-16":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Japan')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
KoreaSouth = pd.merge(confirmed_country["Korea, South"], death["Korea, South"], left_index=True, right_index=True)
KoreaSouth.rename(columns = {'Korea, South_x':'Confirmed Cases'}, inplace = True)
KoreaSouth.rename(columns = {'Korea, South_y':'Death Cases'}, inplace = True)
KoreaSouth["2020-03-02":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Korea, South')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Luxembourg = pd.merge(confirmed_country["Luxembourg"], death["Luxembourg"], left_index=True, right_index=True)
Luxembourg.rename(columns = {'Luxembourg_x':'Confirmed Cases'}, inplace = True)
Luxembourg.rename(columns = {'Luxembourg_y':'Death Cases'}, inplace = True)
Luxembourg["2020-04-01":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Luxembourg')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Malaysia = pd.merge(confirmed_country["Malaysia"], death["Malaysia"], left_index=True, right_index=True)
Malaysia.rename(columns = {'Malaysia_x':'Confirmed Cases'}, inplace = True)
Malaysia.rename(columns = {'Malaysia_y':'Death Cases'}, inplace = True)
Malaysia["2020-03-27":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Malaysia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Mexico = pd.merge(confirmed_country["Mexico"], death["Mexico"], left_index=True, right_index=True)
Mexico.rename(columns = {'Mexico_x':'Confirmed Cases'}, inplace = True)
Mexico.rename(columns = {'Mexico_y':'Death Cases'}, inplace = True)
Mexico["2020-03-31":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Mexico')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Morocco = pd.merge(confirmed_country["Morocco"], death["Morocco"], left_index=True, right_index=True)
Morocco.rename(columns = {'Morocco_x':'Confirmed Cases'}, inplace = True)
Morocco.rename(columns = {'Morocco_y':'Death Cases'}, inplace = True)
Morocco["2020-03-28":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Morocco')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Netherlands = pd.merge(confirmed_country["Netherlands"], death["Netherlands"], left_index=True, right_index=True)
Netherlands.rename(columns = {'Netherlands_x':'Confirmed Cases'}, inplace = True)
Netherlands.rename(columns = {'Netherlands_y':'Death Cases'}, inplace = True)
Netherlands["2020-03-17":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Netherlands')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
NorthMacedonia = pd.merge(confirmed_country["North Macedonia"], death["North Macedonia"], left_index=True, right_index=True)
NorthMacedonia.rename(columns = {'North Macedonia_x':'Confirmed Cases'}, inplace = True)
NorthMacedonia.rename(columns = {'North Macedonia_y':'Death Cases'}, inplace = True)
NorthMacedonia["2020-04-07":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di North Macedonia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Norway = pd.merge(confirmed_country["Norway"], death["Norway"], left_index=True, right_index=True)
Norway.rename(columns = {'Norway_x':'Confirmed Cases'}, inplace = True)
Norway.rename(columns = {'Norway_y':'Death Cases'}, inplace = True)
Norway["2020-03-29":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Norway')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Pakistan = pd.merge(confirmed_country["Pakistan"], death["Pakistan"], left_index=True, right_index=True)
Pakistan.rename(columns = {'Pakistan_x':'Confirmed Cases'}, inplace = True)
Pakistan.rename(columns = {'Pakistan_y':'Death Cases'}, inplace = True)
Pakistan["2020-03-31":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Pakistan')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Panama = pd.merge(confirmed_country["Panama"], death["Panama"], left_index=True, right_index=True)
Panama.rename(columns = {'Panama_x':'Confirmed Cases'}, inplace = True)
Panama.rename(columns = {'Panama_y':'Death Cases'}, inplace = True)
Panama["2020-03-31":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Panama')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Peru = pd.merge(confirmed_country["Peru"], death["Peru"], left_index=True, right_index=True)
Peru.rename(columns = {'Peru_x':'Confirmed Cases'}, inplace = True)
Peru.rename(columns = {'Peru_y':'Death Cases'}, inplace = True)
Peru["2020-03-31":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Peru')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Philippines = pd.merge(confirmed_country["Philippines"], death["Philippines"], left_index=True, right_index=True)
Philippines.rename(columns = {'Philippines_x':'Confirmed Cases'}, inplace = True)
Philippines.rename(columns = {'Philippines_y':'Death Cases'}, inplace = True)
Philippines["2020-03-22":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Philippines')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Poland = pd.merge(confirmed_country["Poland"], death["Poland"], left_index=True, right_index=True)
Poland.rename(columns = {'Poland_x':'Confirmed Cases'}, inplace = True)
Poland.rename(columns = {'Poland_y':'Death Cases'}, inplace = True)
Poland["2020-03-30":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Poland')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Portugal = pd.merge(confirmed_country["Portugal"], death["Portugal"], left_index=True, right_index=True)
Portugal.rename(columns = {'Portugal_x':'Confirmed Cases'}, inplace = True)
Portugal.rename(columns = {'Portugal_y':'Death Cases'}, inplace = True)
Portugal["2020-03-24":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Portugal')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Romania = pd.merge(confirmed_country["Romania"], death["Romania"], left_index=True, right_index=True)
Romania.rename(columns = {'Romania_x':'Confirmed Cases'}, inplace = True)
Romania.rename(columns = {'Romania_y':'Death Cases'}, inplace = True)
Romania["2020-03-27":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Romania')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Russia = pd.merge(confirmed_country["Russia"], deaths["Russia"], left_index=True, right_index=True)
Russia.rename(columns = {'Russia_x':'Confirmed Cases'}, inplace = True)
Russia.rename(columns = {'Russia_y':'Death Cases'}, inplace = True)
Russia["2020-04-02":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Russia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
SanMarino = pd.merge(confirmed_country["San Marino"], death["San Marino"], left_index=True, right_index=True)
SanMarino.rename(columns = {'San Marino_x':'Confirmed Cases'}, inplace = True)
SanMarino.rename(columns = {'San Marino_y':'Death Cases'}, inplace = True)
SanMarino["2020-03-30":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di San Marino')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
SaudiArabia = pd.merge(confirmed_country["Saudi Arabia"], death["Saudi Arabia"], left_index=True, right_index=True)
SaudiArabia.rename(columns = {'Saudi Arabia_x':'Confirmed Cases'}, inplace = True)
SaudiArabia.rename(columns = {'Saudi Arabia_y':'Death Cases'}, inplace = True)
SaudiArabia["2020-04-03":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Saudi Arabia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Serbia = pd.merge(confirmed_country["Serbia"], death["Serbia"], left_index=True, right_index=True)
Serbia.rename(columns = {'Serbia_x':'Confirmed Cases'}, inplace = True)
Serbia.rename(columns = {'Serbia_y':'Death Cases'}, inplace = True)
Serbia["2020-04-01":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Serbia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Slovenia = pd.merge(confirmed_country["Slovenia"], death["Slovenia"], left_index=True, right_index=True)
Slovenia.rename(columns = {'Slovenia_x':'Confirmed Cases'}, inplace = True)
Slovenia.rename(columns = {'Slovenia_y':'Death Cases'}, inplace = True)
Slovenia["2020-04-05":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Slovenia')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Spain = pd.merge(confirmed_country["Spain"], death["Spain"], left_index=True, right_index=True)
Spain.rename(columns = {'Spain_x':'Confirmed Cases'}, inplace = True)
Spain.rename(columns = {'Spain_y':'Death Cases'}, inplace = True)
Spain["2020-03-09":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Spain')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Sweden = pd.merge(confirmed_country["Sweden"], death["Sweden"], left_index=True, right_index=True)
Sweden.rename(columns = {'Sweden_x':'Confirmed Cases'}, inplace = True)
Sweden.rename(columns = {'Sweden_y':'Death Cases'}, inplace = True)
Sweden["2020-03-23":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Sweden')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Switzerland = pd.merge(confirmed_country["Switzerland"], death["Switzerland"], left_index=True, right_index=True)
Switzerland.rename(columns = {'Switzerland_x':'Confirmed Cases'}, inplace = True)
Switzerland.rename(columns = {'Switzerland_y':'Death Cases'}, inplace = True)
Switzerland["2020-03-17":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Switzerland')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Thailand = pd.merge(confirmed_country["Thailand"], death["Thailand"], left_index=True, right_index=True)
Thailand.rename(columns = {'Thailand_x':'Confirmed Cases'}, inplace = True)
Thailand.rename(columns = {'Thailand_y':'Death Cases'}, inplace = True)
Thailand["2020-04-06":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Thailand')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Turkey = pd.merge(confirmed_country["Turkey"], death["Turkey"], left_index=True, right_index=True)
Turkey.rename(columns = {'Turkey_x':'Confirmed Cases'}, inplace = True)
Turkey.rename(columns = {'Turkey_y':'Death Cases'}, inplace = True)
Turkey["2020-03-22":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Turkey')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
US = pd.merge(confirmed_country["US"], death["US"], left_index=True, right_index=True)
US.rename(columns = {'US_x':'Confirmed Cases'}, inplace = True)
US.rename(columns = {'US_y':'Death Cases'}, inplace = True)
US["2020-03-10":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di US')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
Ukraine = pd.merge(confirmed_country["Ukraine"], death["Ukraine"], left_index=True, right_index=True)
Ukraine.rename(columns = {'Ukraine_x':'Confirmed Cases'}, inplace = True)
Ukraine.rename(columns = {'Ukraine_y':'Death Cases'}, inplace = True)
Ukraine["2020-04-03":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di Ukraine')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
UK = pd.merge(confirmed_country["United Kingdom"], death["United Kingdom"], left_index=True, right_index=True)
UK.rename(columns = {'United Kingdom_x':'Confirmed Cases'}, inplace = True)
UK.rename(columns = {'United Kingdom_y':'Death Cases'}, inplace = True)
UK["2020-03-16":"2020-04-07"].plot(linewidth=3)
plt.title('Kasus Kematian COVID-19 di United Kingdom')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Kematian')
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
plt.gcf().set_size_inches(8, 7)
C:\Users\BelindaM\anaconda3\lib\site-packages\pandas\plotting\_matplotlib\core.py:386: RuntimeWarning: More than 20 figures have been opened. Figures created through the pyplot interface (`matplotlib.pyplot.figure`) are retained until explicitly closed and may consume too much memory. (To control this warning, see the rcParam `figure.max_open_warning`). fig = self.plt.figure(figsize=self.figsize)
def data_kematian(raw_data_deaths):
died = raw_data_deaths.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
value_vars=raw_data_deaths.iloc[:,4:],
var_name='Date', value_name='Total Kasus Kematian')
died["Date"] = pd.to_datetime(died["Date"])
died = died.drop(["Province/State","Lat","Long"], axis = 1)
died = died.groupby(['Date','Country/Region'],as_index=False).agg({'Total Kasus Kematian': 'sum'})
return (died)
data_meninggal = data_kematian(raw_data_deaths)
list_nama = list(death.columns)
df=data_meninggal[data_meninggal['Country/Region'].isin(list_nama)]
df['Total Kasus Kematian'] = np.where((df['Total Kasus Kematian'] < 25 ) , 0, df['Total Kasus Kematian'])
C:\Users\BelindaM\AppData\Local\Temp\ipykernel_15068\2344823607.py:14: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy df['Total Kasus Kematian'] = np.where((df['Total Kasus Kematian'] < 25 ) , 0, df['Total Kasus Kematian'])
fig = px.choropleth(df, locations="Country/Region",
color=np.log(df["Total Kasus Kematian"]),
locationmode='country names', hover_name="Country/Region",
animation_frame=df["Date"].dt.strftime('%Y-%m-%d'),
title='Total Kasus Kematian berdasarkan urutan waktu', color_continuous_scale=px.colors.sequential.matter)
fig.update(layout_coloraxis_showscale=False)
fig.show()
C:\Users\BelindaM\anaconda3\lib\site-packages\pandas\core\arraylike.py:397: RuntimeWarning: divide by zero encountered in log result = getattr(ufunc, method)(*inputs, **kwargs)
Berdasarkan hasil visualisasi di atas, negara yang memiliki kasus kematian lebih atau sama dengan 25 berjumlah 57 negara. Negara China merupakan negara pertama dari ke 57 negara yang memiliki kasus kematian lebih dari 25, total kasus kematian China yang pertama berjumlah 17 pada tanggal 22 Januari 2020. Hamir seluruh 52 negara tersebut mengalami peningkatan kasus terkonfirmasi COVID-19 di-iringi kasus kematian yang tidak terlalu berarti . Jumlah kasus kian menaik semestara peningkatan kasus kematian di setiap negara tidak sebanding. Penambahan kasus kematian yang lebih dari 25 baru muncul pada negara selain Cina pada peridoe akhir Februari 2020 (27 Februaru 2020) yakni negara Iran dan Italy.
Kasus kematian COVID-19 dimulai dari benua Asia Timur kemudia mulai merambah ke bagian benua Eropa kemudian menuju benua Amerika, baru setelah itu mulai berpencar ke berbagai belahan dunia. Selain negara China, kasus kematian dari ke 56 negara lainnya baru nampak pada akhir Februari menuju awal Maret ditandai dengan negara Iran dan Italy yang terkonfirmasi memiliki kasus kematian.
Meskipun negara China merupakan negara pertama dengan jumlah kematian yang lebih tinggi di bandingkan dengan negara lainnya, pada akhir periode tampak beberapa negara memiliki kasus yang lebih tinggi ketimbang China, yakni seperti iltalia, Prancis, UK, dan US.
Anda perlu mendapatkan informasi mengenai total pasien COVID-19 yang dinyatakan sembuh seperti pada kasus terkonfirmasi dan kasus kematian tapi, kali ini lakukan hal tersebut dengan membuat user-defined functions yang diperlukan bukan mengetik ulang barisan code yang sama seperti di nomor sebelumnya.
def setindex(recovered):
recovered = recovered.set_index(['Date'])
return recovered
def groupingsum(recovered):
recovered = recovered.groupby(['Date','Country/Region'],as_index=False).agg({'Total Pasien Sembuh': 'sum'})
recovered = recovered.pivot_table(index=['Date'], columns='Country/Region',
values='Total Pasien Sembuh', aggfunc='first').reset_index()
return recovered
def buat_data_baru(raw_data_recovered):
recovered = raw_data_recovered.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
value_vars=raw_data_recovered.iloc[:,4:],
var_name='Date', value_name='Total Pasien Sembuh')
recovered["Date"] = pd.to_datetime(recovered["Date"])
recovered = recovered.drop(["Province/State","Lat","Long"], axis = 1)
recovered = groupingsum(recovered)
recovered = setindex(recovered)
return (recovered)
recovery = buat_data_baru(raw_data_recovered)
Periksa fungsi-fungsi yang sudah anda buat
recovery
| Country/Region | Afghanistan | Albania | Algeria | Andorra | Angola | Antigua and Barbuda | Argentina | Armenia | Australia | Austria | Azerbaijan | Bahamas | Bahrain | Bangladesh | Barbados | Belarus | Belgium | Belize | Benin | Bhutan | Bolivia | Bosnia and Herzegovina | Botswana | Brazil | Brunei | Bulgaria | Burkina Faso | Burma | Burundi | Cabo Verde | Cambodia | Cameroon | Canada | Central African Republic | Chad | Chile | China | Colombia | Comoros | Congo (Brazzaville) | Congo (Kinshasa) | Costa Rica | Cote d'Ivoire | Croatia | Cuba | Cyprus | Czechia | Denmark | Diamond Princess | Djibouti | Dominica | Dominican Republic | Ecuador | Egypt | El Salvador | Equatorial Guinea | Eritrea | Estonia | Eswatini | Ethiopia | Fiji | Finland | France | Gabon | Gambia | Georgia | Germany | Ghana | Greece | Grenada | Guatemala | Guinea | Guinea-Bissau | Guyana | Haiti | Holy See | Honduras | Hungary | Iceland | India | Indonesia | Iran | Iraq | Ireland | Israel | Italy | Jamaica | Japan | Jordan | Kazakhstan | Kenya | Kiribati | Korea, South | Kosovo | Kuwait | Kyrgyzstan | Laos | Latvia | Lebanon | Lesotho | Liberia | Libya | Liechtenstein | Lithuania | Luxembourg | MS Zaandam | Madagascar | Malawi | Malaysia | Maldives | Mali | Malta | Marshall Islands | Mauritania | Mauritius | Mexico | Micronesia | Moldova | Monaco | Mongolia | Montenegro | Morocco | Mozambique | Namibia | Nepal | Netherlands | New Zealand | Nicaragua | Niger | Nigeria | North Macedonia | Norway | Oman | Pakistan | Palau | Panama | Papua New Guinea | Paraguay | Peru | Philippines | Poland | Portugal | Qatar | Romania | Russia | Rwanda | Saint Kitts and Nevis | Saint Lucia | Saint Vincent and the Grenadines | Samoa | San Marino | Sao Tome and Principe | Saudi Arabia | Senegal | Serbia | Seychelles | Sierra Leone | Singapore | Slovakia | Slovenia | Solomon Islands | Somalia | South Africa | South Sudan | Spain | Sri Lanka | Sudan | Summer Olympics 2020 | Suriname | Sweden | Switzerland | Syria | Taiwan* | Tajikistan | Tanzania | Thailand | Timor-Leste | Togo | Tonga | Trinidad and Tobago | Tunisia | Turkey | US | Uganda | Ukraine | United Arab Emirates | United Kingdom | Uruguay | Uzbekistan | Vanuatu | Venezuela | Vietnam | West Bank and Gaza | Yemen | Zambia | Zimbabwe |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Date | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| 2020-01-22 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-23 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 30 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-24 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 36 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-25 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 39 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2020-01-26 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 49 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 2022-01-07 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2022-01-08 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2022-01-09 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2022-01-10 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 2022-01-11 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
721 rows × 196 columns
Selanjutnya,tampilkan data total pasien COVID-19 yang sembuh dalam bentuk grafik yang sesuai. Berikan judul, labels, dan spesifikasi (ukuran, warna, ketebalan, dll) yang sesuai sehingga plot yang dihasilkan rapi, menarik, dan mudah dipahami.
copyrecovery=recovery.copy()
recovery["Total Pasien Sembuh dari COVID-19"] = recovery.sum(axis=1)
recovery.drop(recovery.iloc[:, 0:196], inplace = True, axis = 1)
sembuh = recovery['Total Pasien Sembuh dari COVID-19']
Sembuh = pd.DataFrame(sembuh)
Sembuh
| Total Pasien Sembuh dari COVID-19 | |
|---|---|
| Date | |
| 2020-01-22 | 30 |
| 2020-01-23 | 32 |
| 2020-01-24 | 39 |
| 2020-01-25 | 42 |
| 2020-01-26 | 56 |
| ... | ... |
| 2022-01-07 | 0 |
| 2022-01-08 | 0 |
| 2022-01-09 | 0 |
| 2022-01-10 | 0 |
| 2022-01-11 | 0 |
721 rows × 1 columns
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
Sembuh["2020-01-22":"2022-01-11"].plot(linewidth=3);
plt.title('Kasus Sembuh COVID-19')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Sembuh dalam satuan ratusan juta')
plt.gcf().set_size_inches(12, 8)
def buat_data(raw_data_recovered):
recovered2 = raw_data_recovered.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
value_vars=raw_data_recovered.iloc[:,4:],
var_name='Date', value_name='Total Pasien Sembuh')
recovered2["Date"] = pd.to_datetime(recovered2["Date"])
recovered2 = recovered2.drop(["Province/State","Lat","Long"], axis = 1)
recovered2 = recovered2.groupby(['Date','Country/Region'],as_index=False).agg({'Total Pasien Sembuh': 'sum'})
return (recovered2)
recovery2 = buat_data(raw_data_recovered)
copy_recovery2=recovery2.copy()
recovery2 = recovery2[recovery2["Date"]=="2021-08-04"]
pd.set_option("mode.chained_assignment", None)
fig=px.choropleth(recovery2, locations="Country/Region", locationmode='country names',
color_continuous_scale='dense', color=np.log10(recovery2["Total Pasien Sembuh"]), range_color=(0,10))
fig.show()
C:\Users\BelindaM\anaconda3\lib\site-packages\pandas\core\arraylike.py:397: RuntimeWarning: divide by zero encountered in log10
def buat_dataa(raw_data_recovered):
recovered3 = raw_data_recovered.melt(id_vars=['Province/State','Country/Region','Lat','Long'],
value_vars=raw_data_recovered.iloc[:,4:],
var_name='Date', value_name='Total Pasien Sembuh')
recovered3["Date"] = pd.to_datetime(recovered3["Date"])
recovered3 = recovered3.drop(["Province/State","Lat","Long"], axis = 1)
recovered3 = recovered3.groupby(['Date','Country/Region'],as_index=False).agg({'Total Pasien Sembuh': 'sum'})
return (recovered3)
recovery3 = buat_dataa(raw_data_recovered)
fig = px.choropleth(recovery3, locations="Country/Region",
color=np.log(recovery3["Total Pasien Sembuh"]),
locationmode='country names', hover_name="Country/Region",
animation_frame=recovery3["Date"].dt.strftime('%Y-%m-%d'),
title='Total Kasus Sembuh berdasarkan urutan waktu', color_continuous_scale=px.colors.sequential.matter)
fig.update(layout_coloraxis_showscale=False)
fig.show()
C:\Users\BelindaM\anaconda3\lib\site-packages\pandas\core\arraylike.py:397: RuntimeWarning: divide by zero encountered in log
Data raw_data_recovered terdiri dari 721 pengamatan dimulai dari tanggal 22 Januari 2020 hingga 11 Januari 2022. Dimulai dari tanggal 22 Januari 2020 kasus sembuh di berbagai belahan dunia kian bertambah hingga akhir tahun 2020, nampaknya terdapat penurunan kasus mendadak. Terlepas dari itu, peningkatan kasus sembuh terjad kembali hingga bulan Agustus 2021.Semenjak tanggal 4 Agustus 2021 seluruh isi sel dari data set raw_data_recovered bernilai 0.
Berdasarkan hasil visualisasi di atas didapatkan informasi bahwa kasus pasien sembuh telah dimuali sejak awal periode tampak pada awal periode dan terus mengalami kenaikan hingga periode awal Agustus 2021.Negara dengan kasus sembuh dimulai dari Cina dan Thailand dengan total kasus sembuh sebanyak 28 kasus dari Cina dan 2 kasus dari Thailand. Kasus sembuh berawal dari daerah benua Asia yang kemudian merambat ke benua Australia dan baru itu menjalar ke berbagai belahan dunia.
Meskipun pada awal periode negara Cina tampak memiliki jumlah kasus sembuh terbanyak, namun pada tanggal 4 Agustus 2021 negara dengan jumlah kasus sembuh terbanyak ialah berasal dari negara India disusul dengan negara Amerika Serikat, Brazil dan Rusia. Sebaliknya kasus konfirmasi sembuh terkecil ialah berasal dari negara Tanzania.
Lakukan visualisasi yang sama pada beberapa negara berikut (France, Spain, China, US, Italy, and Australia). Berikan judul, labels, dan spesifikasi (ukuran, warna, ketebalan, dll) yang sesuai sehingga plot yang dihasilkan rapi, menarik, dan mudah dipahami.
enamnegaraa = copyrecovery[["France", "Spain", "China", "US", "Italy", "Australia"]]
enamnegaraa = pd.DataFrame(enamnegaraa)
enamnegaraa
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
fig, axes = plt.subplots(nrows=6, ncols=1)
for i, c in enumerate(enamnegaraa.columns):
enamnegaraa[c].plot(kind='line', ax=axes[i], title=c, xlabel="Tanggal", ylabel="Total Kasus Sembuh", legend=c)
plt.subplots_adjust(hspace = 1.0)
plt.gcf().set_size_inches(10, 28)
plt.style.use('https://github.com/dhaitz/matplotlib-stylesheets/raw/master/pitayasmoothie-light.mplstyle')
enamnegaraa.plot(linewidth=3)
plt.title('Kasus Sembuh COVID-19 di Enam Negara dalam satuan juta')
plt.xlabel('Tanggal')
plt.ylabel('Jumlah Kasus Sembuh dalam satuan juta')
plt.gcf().set_size_inches(12, 8)
Dari visualisasi diatas didapatkan hasil bahawa hampir setiap negara mengalami pertambuhan kasus sembuh COVID-19. Mayoritas setiap negara tidak mengalami pertumbuhan kasus sembuh pada periode Agustus 2021, hanya negara Amerika Serikat saja yang pada akhir tahun 2020 mengalami pemberhentian penambahan kasus hingga januari 2022. Hal ini mungkin menjadi salah satu penyebab mengapa pada kurva kaus sembuh secara global, di akhir tahun 2020 terjadi penurunan kasus sembuh secara mendadak. Selain itu didapatkan pula informasi sebagai berikut: